# 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 .decompx_utils import DecompXConfig, DecompXOutput 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, decompx_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 decompx_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, decompx_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 decompx_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, decompx_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, decompx_ready=decompx_ready, # added by Fayyaz / Modarressi ) attention_output = self.output( self_outputs[0], hidden_states, decompx_ready=decompx_ready, # added by Goro Kobayashi (Edited by Fayyaz / Modarressi) ) # Added by Fayyaz / Modarressi # ------------------------------- if decompx_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, decompx_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 decompx_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, decompx_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 decompx_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, decompx_config: Optional[DecompXConfig] = None, # added by Fayyaz / Modarressi ) -> Tuple[torch.Tensor]: decompx_ready = decompx_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, # decompx_ready=decompx_ready, # ) self_attention_outputs = self.attention( hidden_states, attribution_vectors, attention_mask, head_mask, output_attentions=output_attentions, decompx_ready=decompx_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 decompx_config.include_bias_token else decompx_config.bias_decomp_type intermediate_output, pre_act_hidden_states = self.intermediate(attention_output, decompx_ready=decompx_ready) layer_output, pre_ln2_states = self.output(intermediate_output, attention_output, decompx_ready=decompx_ready) if decompx_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 decompx_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 decompx_config.include_biases: residual_weighted_layer = self.bias_decomposer(accumulated_bias, residual_weighted_layer, bias_decomp_type) if decompx_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=decompx_config.include_biases, bias_decomp_type=bias_decomp_type ) else: post_ln_layer = residual_weighted_layer if decompx_config.include_FFN: post_ffn_layer = self.ffn_decomposer_fast if decompx_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=decompx_config.FFN_approx_type, include_biases=decompx_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 decompx_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=decompx_config.include_biases, bias_decomp_type=bias_decomp_type ) else: post_ln2_layer = pre_ln2_layer new_outputs = DecompXOutput( attention=output_builder(summed_weighted_layer, decompx_config.output_attention), res1=output_builder(residual_weighted_layer, decompx_config.output_res1), LN1=output_builder(post_ln_layer, decompx_config.output_res2), FFN=output_builder(post_ffn_layer, decompx_config.output_FFN), res2=output_builder(pre_ln2_layer, decompx_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, decompx_config: Optional[DecompXConfig] = 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 decompx_config and decompx_config.output_all_layers: all_decompx_outputs = DecompXOutput( attention=() if decompx_config.output_attention else None, res1=() if decompx_config.output_res1 else None, LN1=() if decompx_config.output_LN1 else None, FFN=() if decompx_config.output_LN1 else None, res2=() if decompx_config.output_res2 else None, encoder=() if decompx_config.output_encoder else None, aggregated=() if decompx_config.output_aggregated and decompx_config.aggregation else None, ) else: all_decompx_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, decompx_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 decompx_config: decompx_output = layer_outputs[1] if decompx_config.aggregation == "rollout": if decompx_config.include_classifier_w_pooler: raise Exception("Classifier and pooler could be included in vector aggregation mode") encoder_norms = decompx_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 decompx_config.output_aggregated == "norm": decompx_output.aggregated = (aggregated_encoder_norms,) elif decompx_config.output_aggregated is not None: raise Exception( "Rollout aggregated values are only available in norms. Set output_aggregated to 'norm'.") elif decompx_config.aggregation == "vector": aggregated_encoder_vectors = decompx_output.encoder[0][1] if decompx_config.include_classifier_w_pooler: decompx_output.aggregated = (aggregated_encoder_vectors,) else: decompx_output.aggregated = output_builder(aggregated_encoder_vectors, decompx_config.output_aggregated) decompx_output.encoder = output_builder(decompx_output.encoder[0][1], decompx_config.output_encoder) if decompx_config.output_all_layers: all_decompx_outputs.attention = all_decompx_outputs.attention + decompx_output.attention if decompx_config.output_attention else None all_decompx_outputs.res1 = all_decompx_outputs.res1 + decompx_output.res1 if decompx_config.output_res1 else None all_decompx_outputs.LN1 = all_decompx_outputs.LN1 + decompx_output.LN1 if decompx_config.output_LN1 else None all_decompx_outputs.FFN = all_decompx_outputs.FFN + decompx_output.FFN if decompx_config.output_FFN else None all_decompx_outputs.res2 = all_decompx_outputs.res2 + decompx_output.res2 if decompx_config.output_res2 else None all_decompx_outputs.encoder = all_decompx_outputs.encoder + decompx_output.encoder if decompx_config.output_encoder else None if decompx_config.include_classifier_w_pooler and decompx_config.aggregation == "vector": all_decompx_outputs.aggregated = all_decompx_outputs.aggregated + output_builder( aggregated_encoder_vectors, decompx_config.output_aggregated) if decompx_config.output_aggregated else None else: all_decompx_outputs.aggregated = all_decompx_outputs.aggregated + decompx_output.aggregated if decompx_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, decompx_output if decompx_config else None, all_decompx_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, decompx_config: Optional[DecompXConfig] = 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, decompx_config=decompx_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, decompx_config: Optional[DecompXConfig] = 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, decompx_config=decompx_config ) sequence_output = outputs[0] logits, mid_classifier_outputs = self.classifier(sequence_output, decompx_ready=decompx_config is not None) if decompx_config is not None: pre_act_pooled = mid_classifier_outputs[0] pooled_output = mid_classifier_outputs[1] if decompx_config.include_classifier_w_pooler: decompx_idx = -2 if decompx_config.output_all_layers else -1 aggregated_attribution_vectors = outputs[decompx_idx].aggregated[0] outputs[decompx_idx].aggregated = output_builder(aggregated_attribution_vectors, decompx_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=decompx_config.include_biases, bias_decomp_type="biastoken" if decompx_config.include_bias_token else decompx_config.bias_decomp_type, tanh_approx_type=decompx_config.tanh_approx_type ) aggregated_attribution_vectors = pooler_decomposed outputs[decompx_idx].pooler = output_builder(pooler_decomposed, decompx_config.output_pooler) classifier_decomposed = self.ffn_decomposer( attribution_vectors=aggregated_attribution_vectors, include_biases=decompx_config.include_biases, bias_decomp_type="biastoken" if decompx_config.include_bias_token else decompx_config.bias_decomp_type ) if decompx_config.include_bias_token and decompx_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=decompx_config.bias_decomp_type ) outputs[decompx_idx].classifier = classifier_decomposed if decompx_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, decompx_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 decompx_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