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#############################
# Imports
#############################
# Python modules
from typing import Optional, Tuple
# Remote modules
import torch
from torch import nn
# Local modules
from .attention_utils import update_weights_regarding_relations_on_specific_head
class BartCustomMaskAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
num_relation_kinds: int = 0,
heads_mask: Optional[torch.Tensor] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
if heads_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {heads_mask.size()}"
)
self.heads_mask = heads_mask
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.num_relation_kinds = num_relation_kinds
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
relation_inputs: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = hidden_states.size()
#print(relation_inputs.shape, 'VS ', (bsz, tgt_len, tgt_len))
if relation_inputs is None:
# TODO
relation_inputs = torch.zeros((bsz, tgt_len, tgt_len)).to('cuda').long()
assert relation_inputs.shape == (bsz, tgt_len, tgt_len)
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_cross_attention:
# cross_attentions
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
elif past_key_value is not None:
# reuse k, v, self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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_states, value_states)
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if self.heads_mask is not None:# and layer_head_mask is not None:
if self.heads_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
)
h_mask = layer_head_mask
#print('h_mask: ', h_mask)
if layer_head_mask is None:
h_mask = self.heads_mask
#h_mask.to(attn_weights.device)
attn_weights = update_weights_regarding_relations_on_specific_head(h_mask, attn_weights,
relation_inputs, bsz, self.num_heads, tgt_len,
src_len, verbose=False)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
elif layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is {layer_head_mask.size()}"
)
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
else:
attn_weights_reshaped = None
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
attn_output = torch.bmm(attn_probs, value_states)
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output.size()}"
)
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped, past_key_value
def find_head_to_mask(self, heads_mask) -> int:
head_idx = torch.argmax(heads_mask)
head_idx_simple = head_idx.item()
return head_idx_simple
def create_commonsense_mask(self, bsz, n_tokens, commonsense_matrix, num_heads=16, specific_head=0):
commonsense_mask = torch.zeros(
((bsz, num_heads, n_tokens, n_tokens))
)
if commonsense_matrix is None:
commonsense_matrix = torch.zeros(
((bsz, n_tokens, n_tokens))
)
commonsense_mask = commonsense_mask.reshape((num_heads, bsz, n_tokens, n_tokens))
commonsense_mask[specific_head] = commonsense_matrix
commonsense_mask = commonsense_mask.reshape((bsz, num_heads, n_tokens, n_tokens))
return commonsense_mask
def commonsense_attention_mask_update(self, bsz, n_tokens, commonsense_matrix, attn_weights,
specific_head=0):
num_heads = self.num_heads
commonsense_mask = torch.zeros(
((bsz, num_heads, n_tokens, n_tokens))
)
attn_weights_helper = attn_weights.reshape((num_heads, bsz, n_tokens, n_tokens))
zeros = torch.zeros(
((bsz, n_tokens, n_tokens))
)
head_previous_attention_weights = attn_weights_helper[specific_head]
attn_weights_helper[specific_head] = zeros
attn_weights_helper = attn_weights_helper.reshape((bsz, num_heads, n_tokens, n_tokens))
if commonsense_matrix is None:
# ignore is not passed (ones -> neutral since multiplication is used)
commonsense_matrix = torch.ones(
((bsz, n_tokens, n_tokens))
)
commonsense_mask = commonsense_mask.reshape((num_heads, bsz, n_tokens, n_tokens))
commonsense_mask[specific_head] = head_previous_attention_weights * commonsense_matrix
# TODO Stupid conversion
commonsense_mask = commonsense_mask.reshape((bsz, num_heads, n_tokens, n_tokens)).to('cuda')
return attn_weights_helper + commonsense_mask
def convert_relations_to_binary_mask(self, input_relations):
relations_binary_mask = input_relations.clone()
relations_binary_mask[relations_binary_mask > 1] = 1
return relations_binary_mask
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