diff --git "a/DecompX/src/modeling_roberta.py" "b/DecompX/src/modeling_roberta.py" new file mode 100644--- /dev/null +++ "b/DecompX/src/modeling_roberta.py" @@ -0,0 +1,2127 @@ +# coding=utf-8 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. 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. +"""PyTorch RoBERTa model.""" + +import math +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from packaging import version +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from .globenc_utils import GlobencConfig, GlobencOutput + +from transformers.activations import ACT2FN, gelu +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, + replace_return_docstrings, +) +from transformers.models.roberta.configuration_roberta import RobertaConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "roberta-base" +_CONFIG_FOR_DOC = "RobertaConfig" +_TOKENIZER_FOR_DOC = "RobertaTokenizer" + +ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "roberta-base", + "roberta-large", + "roberta-large-mnli", + "distilroberta-base", + "roberta-base-openai-detector", + "roberta-large-openai-detector", + # See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + +def output_builder(input_vector, output_mode): + if output_mode is None: + return None + elif output_mode == "vector": + return (input_vector,) + elif output_mode == "norm": + return (torch.norm(input_vector, dim=-1),) + elif output_mode == "both": + return ((torch.norm(input_vector, dim=-1), input_vector),) + elif output_mode == "distance_based": + recomposed_vectors = torch.sum(input_vector, dim=-2, keepdim=True) + importance_matrix = -torch.nn.functional.pairwise_distance(input_vector, recomposed_vectors, p=1) + norm_y = torch.norm(recomposed_vectors, dim=-1, p=1) + maxed = torch.maximum(torch.zeros(1, device=norm_y.device), norm_y + importance_matrix) + return (maxed / (torch.sum(maxed, dim=-2, keepdim=True) + 1e-12),) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + 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))) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + + def forward( + self, input_ids=None, token_type_ids=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 input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + else: + return inputs_embeds + + 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) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(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" + ) + 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) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + 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 transpose_for_scores_for_decomposed(self, x): + # x: (B, N, N, H*V) + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + # x: (B, N, N, H, V) + x = x.view(new_x_shape) + # x: (B, H, N, N, V) + return x.permute(0, 3, 1, 2, 4) + + def forward( + self, + hidden_states: torch.Tensor, + attribution_vectors: Optional[torch.FloatTensor] = None, + 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, + globenc_ready: Optional[bool] = None, # added by Fayyaz / Modarressi + ) -> 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 + decomposed_value_layer = 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)) + if attribution_vectors is not None: + decomposed_value_layer = torch.einsum("bijd,vd->bijv", attribution_vectors, self.value.weight) + decomposed_value_layer = self.transpose_for_scores_for_decomposed(decomposed_value_layer) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + 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) + + # 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 + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel 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, 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) + + # added by Fayyaz / Modarressi + # ------------------------------- + if globenc_ready: + outputs = (context_layer, attention_probs, value_layer, decomposed_value_layer) + return outputs + # ------------------------------- + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, + globenc_ready=False): # added by Fayyaz / Modarressi + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + # hidden_states = self.LayerNorm(hidden_states + input_tensor) + pre_ln_states = hidden_states + input_tensor # added by Fayyaz / Modarressi + post_ln_states = self.LayerNorm(pre_ln_states) # added by Fayyaz / Modarressi + # added by Fayyaz / Modarressi + if globenc_ready: + return post_ln_states, pre_ln_states + else: + return post_ln_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention(config, position_embedding_type=position_embedding_type) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + 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: torch.Tensor, + attribution_vectors: Optional[torch.FloatTensor] = None, + 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, + globenc_ready: Optional[bool] = None, # added by Fayyaz / Modarressi + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attribution_vectors, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + globenc_ready=globenc_ready, # added by Fayyaz / Modarressi + ) + attention_output = self.output( + self_outputs[0], + hidden_states, + globenc_ready=globenc_ready, # added by Goro Kobayashi (Edited by Fayyaz / Modarressi) + ) + + # Added by Fayyaz / Modarressi + # ------------------------------- + if globenc_ready: + _, attention_probs, value_layer, decomposed_value_layer = self_outputs + attention_output, pre_ln_states = attention_output + outputs = (attention_output, attention_probs,) + (value_layer, decomposed_value_layer, pre_ln_states) # add attentions and norms if we output them + return outputs + # ------------------------------- + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(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, globenc_ready: Optional[bool] = False) -> torch.Tensor: + pre_act_hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(pre_act_hidden_states) + if globenc_ready: + return hidden_states, pre_act_hidden_states + return hidden_states, None + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, globenc_ready: Optional[bool] = False): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + # hidden_states = self.LayerNorm(hidden_states + input_tensor) + # return hidden_states + # Added by Fayyaz / Modarressi + # ------------------------------- + pre_ln_states = hidden_states + input_tensor + hidden_states = self.LayerNorm(pre_ln_states) + if globenc_ready: + return hidden_states, pre_ln_states + return hidden_states, None + # ------------------------------- + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(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 = RobertaAttention(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 ValueError(f"{self} should be used as a decoder model if cross attention is added") + self.crossattention = RobertaAttention(config, position_embedding_type="absolute") + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + self.similarity_fn = torch.nn.CosineSimilarity(dim=-1) + + 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 + + def bias_decomposer(self, bias, attribution_vectors, bias_decomp_type="absdot"): + # Decomposes the input bias based on similarity to the attribution vectors + # Args: + # bias: a bias vector (all_head_size) + # attribution_vectors: the attribution vectors from token j to i (b, i, j, all_head_size) :: (batch, seq_length, seq_length, all_head_size) + + if bias_decomp_type == "absdot": + weights = torch.abs(torch.einsum("bskd,d->bsk", attribution_vectors, bias)) + elif bias_decomp_type == "abssim": + weights = torch.abs(torch.nn.functional.cosine_similarity(attribution_vectors, bias, dim=-1)) + weights = (torch.norm(attribution_vectors, dim=-1) != 0) * weights + elif bias_decomp_type == "norm": + weights = torch.norm(attribution_vectors, dim=-1) + elif bias_decomp_type == "equal": + weights = (torch.norm(attribution_vectors, dim=-1) != 0) * 1.0 + elif bias_decomp_type == "cls": + weights = torch.zeros(attribution_vectors.shape[:-1], device=attribution_vectors.device) + weights[:,:,0] = 1.0 + elif bias_decomp_type == "dot": + weights = torch.einsum("bskd,d->bsk", attribution_vectors, bias) + elif bias_decomp_type == "biastoken": + attrib_shape = attribution_vectors.shape + if attrib_shape[1] == attrib_shape[2]: + attribution_vectors = torch.concat([attribution_vectors, torch.zeros((attrib_shape[0], attrib_shape[1], 1, attrib_shape[3]), device=attribution_vectors.device)], dim=-2) + attribution_vectors[:,:,-1] = attribution_vectors[:,:,-1] + bias + return attribution_vectors + + weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12) + weighted_bias = torch.matmul(weights.unsqueeze(dim=-1), bias.unsqueeze(dim=0)) + return attribution_vectors + weighted_bias + + + def ln_decomposer(self, attribution_vectors, pre_ln_states, gamma, beta, eps, include_biases=True, bias_decomp_type="absdot"): + mean = pre_ln_states.mean(-1, keepdim=True) # (batch, seq_len, 1) m(y=Σy_j) + var = (pre_ln_states - mean).pow(2).mean(-1, keepdim=True).unsqueeze(dim=2) # (batch, seq_len, 1, 1) s(y) + + each_mean = attribution_vectors.mean(-1, keepdim=True) # (batch, seq_len, seq_len, 1) m(y_j) + + normalized_layer = torch.div(attribution_vectors - each_mean, + (var + eps) ** (1 / 2)) # (batch, seq_len, seq_len, all_head_size) + + post_ln_layer = torch.einsum('bskd,d->bskd', normalized_layer, + gamma) # (batch, seq_len, seq_len, all_head_size) + + if include_biases: + return self.bias_decomposer(beta, post_ln_layer, bias_decomp_type=bias_decomp_type) + else: + return post_ln_layer + + + def gelu_linear_approximation(self, intermediate_hidden_states, intermediate_output): + def phi(x): + return (1 + torch.erf(x / math.sqrt(2))) / 2. + + def normal_pdf(x): + return torch.exp(-(x**2) / 2) / math.sqrt(2. * math.pi) + + def gelu_deriv(x): + return phi(x)+x*normal_pdf(x) + + m = gelu_deriv(intermediate_hidden_states) + b = intermediate_output - m * intermediate_hidden_states + return m, b + + + def gelu_decomposition(self, attribution_vectors, intermediate_hidden_states, intermediate_output, bias_decomp_type): + m, b = self.gelu_linear_approximation(intermediate_hidden_states, intermediate_output) + mx = attribution_vectors * m.unsqueeze(dim=-2) + + if bias_decomp_type == "absdot": + weights = torch.abs(torch.einsum("bskl,bsl->bsk", mx, b)) + elif bias_decomp_type == "abssim": + weights = torch.abs(torch.nn.functional.cosine_similarity(mx, b)) + weights = (torch.norm(mx, dim=-1) != 0) * weights + elif bias_decomp_type == "norm": + weights = torch.norm(mx, dim=-1) + elif bias_decomp_type == "equal": + weights = (torch.norm(mx, dim=-1) != 0) * 1.0 + elif bias_decomp_type == "cls": + weights = torch.zeros(mx.shape[:-1], device=mx.device) + weights[:,:,0] = 1.0 + + weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12) + weighted_bias = torch.einsum("bsl,bsk->bskl", b, weights) + return mx + weighted_bias + + + def gelu_zo_decomposition(self, attribution_vectors, intermediate_hidden_states, intermediate_output): + m = intermediate_output / (intermediate_hidden_states + 1e-12) + mx = attribution_vectors * m.unsqueeze(dim=-2) + return mx + + + def ffn_decomposer(self, attribution_vectors, intermediate_hidden_states, intermediate_output, include_biases=True, approximation_type="GeLU_LA", bias_decomp_type="absdot"): + post_first_layer = torch.einsum("ld,bskd->bskl", self.intermediate.dense.weight, attribution_vectors) + if include_biases: + post_first_layer = self.bias_decomposer(self.intermediate.dense.bias, post_first_layer, bias_decomp_type=bias_decomp_type) + + if approximation_type == "ReLU": + mask_for_gelu_approx = (intermediate_hidden_states > 0) + post_act_first_layer = torch.einsum("bskl, bsl->bskl", post_first_layer, mask_for_gelu_approx) + post_act_first_layer = post_first_layer * mask_for_gelu_approx.unsqueeze(dim=-2) + elif approximation_type == "GeLU_LA": + post_act_first_layer = self.gelu_decomposition(post_first_layer, intermediate_hidden_states, intermediate_output, bias_decomp_type=bias_decomp_type) + elif approximation_type == "GeLU_ZO": + post_act_first_layer = self.gelu_zo_decomposition(post_first_layer, intermediate_hidden_states, intermediate_output) + + post_second_layer = torch.einsum("bskl, dl->bskd", post_act_first_layer, self.output.dense.weight) + if include_biases: + post_second_layer = self.bias_decomposer(self.output.dense.bias, post_second_layer, bias_decomp_type=bias_decomp_type) + + return post_second_layer + + + def ffn_decomposer_fast(self, attribution_vectors, intermediate_hidden_states, intermediate_output, include_biases=True, approximation_type="GeLU_LA", bias_decomp_type="absdot"): + if approximation_type == "ReLU": + theta = (intermediate_hidden_states > 0) + elif approximation_type == "GeLU_ZO": + theta = intermediate_output / (intermediate_hidden_states + 1e-12) + + scaled_W1 = torch.einsum("bsl,ld->bsld", theta, self.intermediate.dense.weight) + W_equiv = torch.einsum("bsld, zl->bszd", scaled_W1, self.output.dense.weight) + + post_ffn_layer = torch.einsum("bszd,bskd->bskz", W_equiv, attribution_vectors) + + if include_biases: + scaled_b1 = torch.einsum("bsl,l->bsl", theta, self.intermediate.dense.bias) + b_equiv = torch.einsum("bsl, dl->bsd", scaled_b1, self.output.dense.weight) + b_equiv = b_equiv + self.output.dense.bias + + if bias_decomp_type == "absdot": + weights = torch.abs(torch.einsum("bskd,bsd->bsk", post_ffn_layer, b_equiv)) + elif bias_decomp_type == "abssim": + weights = torch.abs(torch.nn.functional.cosine_similarity(post_ffn_layer, b_equiv)) + weights = (torch.norm(post_ffn_layer, dim=-1) != 0) * weights + elif bias_decomp_type == "norm": + weights = torch.norm(post_ffn_layer, dim=-1) + elif bias_decomp_type == "equal": + weights = (torch.norm(post_ffn_layer, dim=-1) != 0) * 1.0 + + weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12) + weighted_bias = torch.einsum("bsd,bsk->bskd", b_equiv, weights) + + post_ffn_layer = post_ffn_layer + weighted_bias + + return post_ffn_layer + + + def forward( + self, + hidden_states: torch.Tensor, + attribution_vectors: Optional[torch.FloatTensor] = None, + 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, + globenc_config: Optional[GlobencConfig] = None, # added by Fayyaz / Modarressi + ) -> Tuple[torch.Tensor]: + globenc_ready = globenc_config is not None + # 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, + # attribution_vectors, + # attention_mask, + # head_mask, + # output_attentions=output_attentions, + # past_key_value=self_attn_past_key_value, + # globenc_ready=globenc_ready, + # ) + self_attention_outputs = self.attention( + hidden_states, + attribution_vectors, + attention_mask, + head_mask, + output_attentions=output_attentions, + globenc_ready=globenc_ready, + ) # changed by Goro Kobayashi + 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 ValueError( + 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 = apply_chunking_to_forward( + # self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + # ) + + # Added by Fayyaz / Modarressi + # ------------------------------- + bias_decomp_type = "biastoken" if globenc_config.include_bias_token else globenc_config.bias_decomp_type + intermediate_output, pre_act_hidden_states = self.intermediate(attention_output, globenc_ready=globenc_ready) + layer_output, pre_ln2_states = self.output(intermediate_output, attention_output, globenc_ready=globenc_ready) + if globenc_ready: + attention_probs, value_layer, decomposed_value_layer, pre_ln_states = outputs + + headmixing_weight = self.attention.output.dense.weight.view(self.all_head_size, self.num_attention_heads, + self.attention_head_size) + + if decomposed_value_layer is None or globenc_config.aggregation != "vector": + transformed_layer = torch.einsum('bhsv,dhv->bhsd', value_layer, headmixing_weight) # V * W^o (z=(qk)v) + # Make weighted vectors αf(x) from transformed vectors (transformed_layer) + # and attention weights (attentions): + # (batch, num_heads, seq_length, seq_length, all_head_size) + weighted_layer = torch.einsum('bhks,bhsd->bhksd', attention_probs, + transformed_layer) # attention_probs(Q*K^t) * V * W^o + # Sum each weighted vectors αf(x) over all heads: + # (batch, seq_length, seq_length, all_head_size) + summed_weighted_layer = weighted_layer.sum(dim=1) # sum over heads + + # Make residual matrix (batch, seq_length, seq_length, all_head_size) + hidden_shape = hidden_states.size() # (batch, seq_length, all_head_size) + device = hidden_states.device + residual = torch.einsum('sk,bsd->bskd', torch.eye(hidden_shape[1]).to(device), + hidden_states) # diagonal representations (hidden states) + + # Make matrix of summed weighted vector + residual vectors + residual_weighted_layer = summed_weighted_layer + residual + accumulated_bias = self.attention.output.dense.bias + else: + transformed_layer = torch.einsum('bhsqv,dhv->bhsqd', decomposed_value_layer, headmixing_weight) + + weighted_layer = torch.einsum('bhks,bhsqd->bhkqd', attention_probs, + transformed_layer) # attention_probs(Q*K^t) * V * W^o + + summed_weighted_layer = weighted_layer.sum(dim=1) # sum over heads + + residual_weighted_layer = summed_weighted_layer + attribution_vectors + accumulated_bias = torch.matmul(self.attention.output.dense.weight, self.attention.self.value.bias) + self.attention.output.dense.bias + + if globenc_config.include_biases: + residual_weighted_layer = self.bias_decomposer(accumulated_bias, residual_weighted_layer, bias_decomp_type) + + if globenc_config.include_LN1: + post_ln_layer = self.ln_decomposer( + attribution_vectors=residual_weighted_layer, + pre_ln_states=pre_ln_states, + gamma=self.attention.output.LayerNorm.weight.data, + beta=self.attention.output.LayerNorm.bias.data, + eps=self.attention.output.LayerNorm.eps, + include_biases=globenc_config.include_biases, + bias_decomp_type=bias_decomp_type + ) + else: + post_ln_layer = residual_weighted_layer + + if globenc_config.include_FFN: + post_ffn_layer = self.ffn_decomposer_fast if globenc_config.FFN_fast_mode else self.ffn_decomposer( + attribution_vectors=post_ln_layer, + intermediate_hidden_states=pre_act_hidden_states, + intermediate_output=intermediate_output, + approximation_type=globenc_config.FFN_approx_type, + include_biases=globenc_config.include_biases, + bias_decomp_type=bias_decomp_type + ) + pre_ln2_layer = post_ln_layer + post_ffn_layer + else: + pre_ln2_layer = post_ln_layer + post_ffn_layer = None + + if globenc_config.include_LN2: + post_ln2_layer = self.ln_decomposer( + attribution_vectors=pre_ln2_layer, + pre_ln_states=pre_ln2_states, + gamma=self.output.LayerNorm.weight.data, + beta=self.output.LayerNorm.bias.data, + eps=self.output.LayerNorm.eps, + include_biases=globenc_config.include_biases, + bias_decomp_type=bias_decomp_type + ) + else: + post_ln2_layer = pre_ln2_layer + + new_outputs = GlobencOutput( + attention=output_builder(summed_weighted_layer, globenc_config.output_attention), + res1=output_builder(residual_weighted_layer, globenc_config.output_res1), + LN1=output_builder(post_ln_layer, globenc_config.output_res2), + FFN=output_builder(post_ffn_layer, globenc_config.output_FFN), + res2=output_builder(pre_ln2_layer, globenc_config.output_res2), + encoder=output_builder(post_ln2_layer, "both") + ) + return (layer_output,) + (new_outputs,) + # ------------------------------- + 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 + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([RobertaLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + 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_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + globenc_config: Optional[GlobencConfig] = None, # added by Fayyaz / Modarressi + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + 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 + + aggregated_encoder_norms = None # added by Fayyaz / Modarressi + aggregated_encoder_vectors = None # added by Fayyaz / Modarressi + + # -- added by Fayyaz / Modarressi + if globenc_config and globenc_config.output_all_layers: + all_globenc_outputs = GlobencOutput( + attention=() if globenc_config.output_attention else None, + res1=() if globenc_config.output_res1 else None, + LN1=() if globenc_config.output_LN1 else None, + FFN=() if globenc_config.output_LN1 else None, + res2=() if globenc_config.output_res2 else None, + encoder=() if globenc_config.output_encoder else None, + aggregated=() if globenc_config.output_aggregated and globenc_config.aggregation else None, + ) + else: + all_globenc_outputs = None + # -- added by Fayyaz / Modarressi + + 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: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + layer_outputs = layer_module( + hidden_states, + aggregated_encoder_vectors, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + globenc_config # added by Fayyaz / Modarressi + ) + + hidden_states = layer_outputs[0] + if use_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],) + + # added by Fayyaz / Modarressi + if globenc_config: + globenc_output = layer_outputs[1] + if globenc_config.aggregation == "rollout": + if globenc_config.include_classifier_w_pooler: + raise Exception("Classifier and pooler could be included in vector aggregation mode") + + encoder_norms = globenc_output.encoder[0][0] + + if aggregated_encoder_norms is None: + aggregated_encoder_norms = encoder_norms * torch.exp(attention_mask).view((-1, attention_mask.shape[-1], 1)) + else: + aggregated_encoder_norms = torch.einsum("ijk,ikm->ijm", encoder_norms, aggregated_encoder_norms) + + if globenc_config.output_aggregated == "norm": + globenc_output.aggregated = (aggregated_encoder_norms,) + elif globenc_config.output_aggregated is not None: + raise Exception("Rollout aggregated values are only available in norms. Set output_aggregated to 'norm'.") + + + elif globenc_config.aggregation == "vector": + aggregated_encoder_vectors = globenc_output.encoder[0][1] + + if globenc_config.include_classifier_w_pooler: + globenc_output.aggregated = (aggregated_encoder_vectors,) + else: + globenc_output.aggregated = output_builder(aggregated_encoder_vectors, globenc_config.output_aggregated) + + globenc_output.encoder = output_builder(globenc_output.encoder[0][1], globenc_config.output_encoder) + + if globenc_config.output_all_layers: + all_globenc_outputs.attention = all_globenc_outputs.attention + globenc_output.attention if globenc_config.output_attention else None + all_globenc_outputs.res1 = all_globenc_outputs.res1 + globenc_output.res1 if globenc_config.output_res1 else None + all_globenc_outputs.LN1 = all_globenc_outputs.LN1 + globenc_output.LN1 if globenc_config.output_LN1 else None + all_globenc_outputs.FFN = all_globenc_outputs.FFN + globenc_output.FFN if globenc_config.output_FFN else None + all_globenc_outputs.res2 = all_globenc_outputs.res2 + globenc_output.res2 if globenc_config.output_res2 else None + all_globenc_outputs.encoder = all_globenc_outputs.encoder + globenc_output.encoder if globenc_config.output_encoder else None + + if globenc_config.include_classifier_w_pooler and globenc_config.aggregation == "vector": + all_globenc_outputs.aggregated = all_globenc_outputs.aggregated + output_builder(aggregated_encoder_vectors, globenc_config.output_aggregated) if globenc_config.output_aggregated else None + else: + all_globenc_outputs.aggregated = all_globenc_outputs.aggregated + globenc_output.aggregated if globenc_config.output_aggregated else None + + 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, + globenc_output if globenc_config else None, + all_globenc_outputs + ] + 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, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(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] + pre_pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pre_pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # 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) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore] + self._keys_to_ignore_on_load_missing = [ + k for k in self._keys_to_ignore_on_load_missing if k not in del_keys_to_ignore + ] + + +ROBERTA_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 ([`RobertaConfig`]): 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. +""" + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. 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) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + 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 [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +@add_start_docstrings( + "The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.", + ROBERTA_START_DOCSTRING, +) +class RobertaModel(RobertaPreTrainedModel): + """ + + 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*_ 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. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # 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(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + processor_class=_TOKENIZER_FOR_DOC, + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPoolingAndCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: 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, + globenc_config: Optional[GlobencConfig] = None, # added by Fayyaz / Modarressi + ) -> 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: + 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) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, 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, device) + + # 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, + token_type_ids=token_type_ids, + 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, + globenc_config=globenc_config, # added by Fayyaz / Modarressi + ) + 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, + ) + + +@add_start_docstrings( + """RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.""", ROBERTA_START_DOCSTRING +) +class RobertaForCausalLM(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if not config.is_decoder: + logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`") + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.lm_head = RobertaLMHead(config) + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + 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(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + past_key_values: Tuple[Tuple[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, CausalLMOutputWithCrossAttentions]: + 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**. + + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). 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]` + 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`). + + Returns: + + Example: + + ```python + >>> from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig + >>> import torch + + >>> tokenizer = RobertaTokenizer.from_pretrained("roberta-base") + >>> config = RobertaConfig.from_pretrained("roberta-base") + >>> config.is_decoder = True + >>> model = RobertaForCausalLM.from_pretrained("roberta-base", config=config) + + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> prediction_logits = outputs.logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if labels is not None: + use_cache = False + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_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 = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss() + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): + input_shape = input_ids.shape + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_shape) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past} + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past + + +@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top.""", ROBERTA_START_DOCSTRING) +class RobertaForMaskedLM(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.lm_head = RobertaLMHead(config) + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + 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(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + processor_class=_TOKENIZER_FOR_DOC, + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=MaskedLMOutput, + config_class=_CONFIG_FOR_DOC, + mask="", + expected_output="' Paris'", + expected_loss=0.1, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: 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, 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.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + 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() + 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, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +@add_start_docstrings( + """ + RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the + pooled output) e.g. for GLUE tasks. + """, + ROBERTA_START_DOCSTRING, +) +class RobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def tanh_linear_approximation(self, pre_act_pooled, post_act_pooled): + def tanh_deriv(x): + return 1 - torch.tanh(x)**2.0 + + m = tanh_deriv(pre_act_pooled) + b = post_act_pooled - m * pre_act_pooled + return m, b + + def tanh_la_decomposition(self, attribution_vectors, pre_act_pooled, post_act_pooled, bias_decomp_type): + m, b = self.tanh_linear_approximation(pre_act_pooled, post_act_pooled) + mx = attribution_vectors * m.unsqueeze(dim=-2) + + if bias_decomp_type == "absdot": + weights = torch.abs(torch.einsum("bkd,bd->bk", mx, b)) + elif bias_decomp_type == "abssim": + weights = torch.abs(torch.nn.functional.cosine_similarity(mx, b, dim=-1)) + weights = (torch.norm(mx, dim=-1) != 0) * weights + elif bias_decomp_type == "norm": + weights = torch.norm(mx, dim=-1) + elif bias_decomp_type == "equal": + weights = (torch.norm(mx, dim=-1) != 0) * 1.0 + elif bias_decomp_type == "cls": + weights = torch.zeros(mx.shape[:-1], device=mx.device) + weights[:,0] = 1.0 + weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12) + weighted_bias = torch.einsum("bd,bk->bkd", b, weights) + return mx + weighted_bias + + def tanh_zo_decomposition(self, attribution_vectors, pre_act_pooled, post_act_pooled): + m = post_act_pooled / (pre_act_pooled + 1e-12) + mx = attribution_vectors * m.unsqueeze(dim=-2) + return mx + + def pooler_decomposer(self, attribution_vectors, pre_act_pooled, post_act_pooled, include_biases=True, bias_decomp_type="absdot", tanh_approx_type="LA"): + post_pool = torch.einsum("ld,bsd->bsl", self.classifier.dense.weight, attribution_vectors) + if include_biases: + post_pool = self.bias_decomposer(self.classifier.dense.bias, post_pool, bias_decomp_type=bias_decomp_type) + + if tanh_approx_type == "LA": + post_act_pool = self.tanh_la_decomposition(post_pool, pre_act_pooled, post_act_pooled, bias_decomp_type=bias_decomp_type) + else: + post_act_pool = self.tanh_zo_decomposition(post_pool, pre_act_pooled, post_act_pooled) + + return post_act_pool + + def bias_decomposer(self, bias, attribution_vectors, bias_decomp_type="absdot"): + # Decomposes the input bias based on similarity to the attribution vectors + # Args: + # bias: a bias vector (all_head_size) + # attribution_vectors: the attribution vectors from token j to i (b, i, j, all_head_size) :: (batch, seq_length, seq_length, all_head_size) + if bias_decomp_type == "absdot": + weights = torch.abs(torch.einsum("bkd,d->bk", attribution_vectors, bias)) + elif bias_decomp_type == "abssim": + weights = torch.abs(torch.nn.functional.cosine_similarity(attribution_vectors, bias, dim=-1)) + weights = (torch.norm(attribution_vectors, dim=-1) != 0) * weights + elif bias_decomp_type == "norm": + weights = torch.norm(attribution_vectors, dim=-1) + elif bias_decomp_type == "equal": + weights = (torch.norm(attribution_vectors, dim=-1) != 0) * 1.0 + elif bias_decomp_type == "cls": + weights = torch.zeros(attribution_vectors.shape[:-1], device=attribution_vectors.device) + weights[:,0] = 1.0 + elif bias_decomp_type == "dot": + weights = torch.einsum("bkd,d->bk", attribution_vectors, bias) + elif bias_decomp_type == "biastoken": + attribution_vectors[:,-1] = attribution_vectors[:,-1] + bias + return attribution_vectors + + weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12) + weighted_bias = torch.matmul(weights.unsqueeze(dim=-1), bias.unsqueeze(dim=0)) + return attribution_vectors + weighted_bias + + def biastoken_decomposer(self, biastoken, attribution_vectors, bias_decomp_type="absdot"): + # Decomposes the input bias based on similarity to the attribution vectors + # Args: + # bias: a bias vector (all_head_size) + # attribution_vectors: the attribution vectors from token j to i (b, i, j, all_head_size) :: (batch, seq_length, seq_length, all_head_size) + if bias_decomp_type == "absdot": + weights = torch.abs(torch.einsum("bkd,bd->bk", attribution_vectors, biastoken)) + elif bias_decomp_type == "abssim": + weights = torch.abs(torch.nn.functional.cosine_similarity(attribution_vectors, biastoken, dim=-1)) + weights = (torch.norm(attribution_vectors, dim=-1) != 0) * weights + elif bias_decomp_type == "norm": + weights = torch.norm(attribution_vectors, dim=-1) + elif bias_decomp_type == "equal": + weights = (torch.norm(attribution_vectors, dim=-1) != 0) * 1.0 + elif bias_decomp_type == "cls": + weights = torch.zeros(attribution_vectors.shape[:-1], device=attribution_vectors.device) + weights[:,0] = 1.0 + elif bias_decomp_type == "dot": + weights = torch.einsum("bkd,d->bk", attribution_vectors, biastoken) + + weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-12) + weighted_bias = torch.matmul(weights.unsqueeze(dim=-1), biastoken.unsqueeze(dim=1)) + return attribution_vectors + weighted_bias + + def ffn_decomposer(self, attribution_vectors, include_biases=True, bias_decomp_type="absdot"): + post_classifier = torch.einsum("ld,bkd->bkl", self.classifier.out_proj.weight, attribution_vectors) + if include_biases: + post_classifier = self.bias_decomposer(self.classifier.out_proj.bias, post_classifier, bias_decomp_type=bias_decomp_type) + + return post_classifier + + @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + processor_class=_TOKENIZER_FOR_DOC, + checkpoint="cardiffnlp/twitter-roberta-base-emotion", + output_type=SequenceClassifierOutput, + config_class=_CONFIG_FOR_DOC, + expected_output="'optimism'", + expected_loss=0.08, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = 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, + globenc_config: Optional[GlobencConfig] = None, # added by Fayyaz / Modarressi + ) -> 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.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + 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, + globenc_config=globenc_config + ) + sequence_output = outputs[0] + logits, mid_classifier_outputs = self.classifier(sequence_output, globenc_ready=globenc_config is not None) + + if globenc_config is not None: + pre_act_pooled = mid_classifier_outputs[0] + pooled_output = mid_classifier_outputs[1] + + if globenc_config.include_classifier_w_pooler: + globenc_idx = -2 if globenc_config.output_all_layers else -1 + aggregated_attribution_vectors = outputs[globenc_idx].aggregated[0] + + outputs[globenc_idx].aggregated = output_builder(aggregated_attribution_vectors, globenc_config.output_aggregated) + + pooler_decomposed = self.pooler_decomposer( + attribution_vectors=aggregated_attribution_vectors[:, 0], + pre_act_pooled=pre_act_pooled, + post_act_pooled=pooled_output, + include_biases=globenc_config.include_biases, + bias_decomp_type="biastoken" if globenc_config.include_bias_token else globenc_config.bias_decomp_type, + tanh_approx_type=globenc_config.tanh_approx_type + ) + + aggregated_attribution_vectors = pooler_decomposed + + outputs[globenc_idx].pooler = output_builder(pooler_decomposed, globenc_config.output_pooler) + + classifier_decomposed = self.ffn_decomposer( + attribution_vectors=aggregated_attribution_vectors, + include_biases=globenc_config.include_biases, + bias_decomp_type="biastoken" if globenc_config.include_bias_token else globenc_config.bias_decomp_type + ) + + if globenc_config.include_bias_token and globenc_config.bias_decomp_type is not None: + bias_token = classifier_decomposed[:,-1,:].detach().clone() + classifier_decomposed = classifier_decomposed[:,:-1,:] + classifier_decomposed = self.biastoken_decomposer( + bias_token, + classifier_decomposed, + bias_decomp_type=globenc_config.bias_decomp_type + ) + + outputs[globenc_idx].classifier = classifier_decomposed if globenc_config.output_classifier else None + + loss = None + if labels is not None: + 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( + """ + Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a + softmax) e.g. for RocStories/SWAG tasks. + """, + ROBERTA_START_DOCSTRING, +) +class RobertaForMultipleChoice(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + + self.roberta = RobertaModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) + @add_code_sample_docstrings( + processor_class=_TOKENIZER_FOR_DOC, + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=MultipleChoiceModelOutput, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MultipleChoiceModelOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + flat_inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + outputs = self.roberta( + flat_input_ids, + position_ids=flat_position_ids, + token_type_ids=flat_token_type_ids, + attention_mask=flat_attention_mask, + head_mask=head_mask, + inputs_embeds=flat_inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + if not return_dict: + output = (reshaped_logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@add_start_docstrings( + """ + Roberta 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. + """, + ROBERTA_START_DOCSTRING, +) +class RobertaForTokenClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roberta = RobertaModel(config, add_pooling_layer=False) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + processor_class=_TOKENIZER_FOR_DOC, + checkpoint="Jean-Baptiste/roberta-large-ner-english", + output_type=TokenClassifierOutput, + config_class=_CONFIG_FOR_DOC, + expected_output="['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']", + expected_loss=0.01, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = 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.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + 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() + 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 RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, globenc_ready=False, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + pre_act = self.dense(x) + post_act = torch.tanh(pre_act) + x = self.dropout(post_act) + x = self.out_proj(x) + if globenc_ready: + return x, (pre_act, post_act) + return x, None + + +@add_start_docstrings( + """ + Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear + layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + """, + ROBERTA_START_DOCSTRING, +) +class RobertaForQuestionAnswering(RobertaPreTrainedModel): + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + @add_code_sample_docstrings( + processor_class=_TOKENIZER_FOR_DOC, + checkpoint="deepset/roberta-base-squad2", + output_type=QuestionAnsweringModelOutput, + config_class=_CONFIG_FOR_DOC, + expected_output="' puppet'", + expected_loss=0.86, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, QuestionAnsweringModelOutput]: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + 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.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +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 \ No newline at end of file