DecompX / DecompX /src /modeling_roberta.py
mohsenfayyaz's picture
Upload 3 files
e654c3a
# 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="<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,