# Copyright 2022 Cerebras Systems.
#
# 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.
import math
import torch
import torch.nn as nn
from modelzoo.common.pytorch.model_utils.create_initializer import (
create_initializer,
)
[docs]class RelativePositionEmbeddingLayer(nn.Module):
r"""Relative Position Embedding Layer
Args:
num_heads (int): number of attention heads.
relative_attention_bias (Tensor): Tensor with relative attention weights.
Shape: [`num_relative_attention_buckets`, `num_heads`]. Defaults set to None.
num_relative_attention_buckets (int): Number of buckets used to calculate relative
position bias. Default: `32`
max_relative_positions (int): The maximum relative distance used when calculating
relative position buckets. See `relative_position_bucket` docs for more details.
Default: `128`
bidirectional_relative_attention (bool): Whether attention is bidirectional.
allow_negative_buckets (bool): If enabled, position buckets will be both positive
and negative (as required by certain models like DEBERTA). Default: `False`.
relative_attn_bias_initializer (bool): Relative Attention bias initializer.
Defaults to ``xavier_uniform``.
Returns:
position_bias (Tensor): Relative position bias, to be used in attention masking
"""
[docs] def __init__(
self,
num_heads,
relative_attention_bias=None,
num_relative_attention_buckets=32,
max_relative_positions=128,
bidirectional_relative_attention=False,
allow_negative_buckets=False,
relative_attn_bias_initializer="xavier_uniform",
):
super(RelativePositionEmbeddingLayer, self).__init__()
if relative_attention_bias:
self.relative_attention_bias = relative_attention_bias
else:
# If no relative attention bias weights are provided when we create them.
self.relative_attention_bias = nn.Embedding(
num_relative_attention_buckets, num_heads
)
self.num_relative_attention_buckets = num_relative_attention_buckets
self.max_relative_positions = max_relative_positions
self.bidirectional_relative_attention = bidirectional_relative_attention
self.allow_negative_buckets = allow_negative_buckets
self.relative_attn_bias_initializer = relative_attn_bias_initializer
self.__reset_parameters()
def reset_parameters(self):
self.__reset_parameters()
def __reset_parameters(self):
weight_initializer = create_initializer(
self.relative_attn_bias_initializer
)
weight_initializer(self.relative_attention_bias.weight.data)
[docs] def forward(
self, seq_length, key_length, past_kv=None,
):
"""Return the position bias.
Args:
seq_length (int): the length of query tokens.
key_length (int): the length of key tokens.
Returns:
Position bias tensor with shape [num_heads, query_length, key_length]
"""
position_bias = self._compute_bias(seq_length, key_length)
# if key and values are already calculated we want only
# the last query position bias
if past_kv is not None:
position_bias = position_bias[:, :, -seq_length, :]
return position_bias
@staticmethod
def compute_raw_relative_positions(query_length, key_length, device=None):
context_position = torch.arange(query_length, device=device)[:, None]
memory_position = torch.arange(key_length, device=device)[None, :]
# shape (query_length, key_length)
relative_position = memory_position - context_position
return relative_position
[docs] @staticmethod
def relative_position_bucket(
relative_position,
bidirectional=True,
num_buckets=32,
max_distance=128,
allow_negative_buckets=False,
):
"""
Translate relative position to a bucket number for relative attention.
The relative position is defined as memory_position - query_position,
i.e. the distance in tokens from the attending position to the attended-to position.
If ``bidirectional_relative_attention`` = False, then positive relative
positions are invalid. We use smaller buckets for small absolute
relative positions and larger buckets for larger absolute relative positions.
All relative positions >= max_distance map to the same bucket.
All relative positions <= -max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences
than the model has been trained on.
Args:
relative_position (Tensor): Tensor with relative positions.
bidirectional (bool): Whether attention is bidirectional
num_buckets (int): number of buckets for relative positions
max_distance (int): Used in order to calculate relative position buckets.
allow_negative_buckets (bool): If enabled, position buckets will be both positive
and negative (as required by certain models like DEBERTA). Default: `False`.
Returns:
a Tensor with the same shape as ``relative_position``,
containing int32 values in the range [0, num_relative_attention_buckets).
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
if allow_negative_buckets:
sign = torch.sign(relative_position)
else:
relative_buckets += (relative_position > 0) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(
relative_position, torch.zeros_like(relative_position)
)
# now relative position is in the range [0, inf)
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# other half of the buckets are for logarithmically bigger bins
# in position up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_position_if_large = torch.min(
relative_position_if_large,
torch.full_like(relative_position_if_large, num_buckets - 1),
)
relative_buckets += torch.where(
is_small, relative_position, relative_position_if_large
)
if allow_negative_buckets:
relative_buckets = relative_buckets * sign
# cast to int32_t because WS weight host gather can only handle int32
return relative_buckets.to(torch.int32)
def compute_relative_positions(self, query_length, key_length):
device = self.relative_attention_bias.weight.device
relative_position = self.compute_raw_relative_positions(
query_length, key_length, device=device
)
if self.num_relative_attention_buckets is not None:
relative_position = self.relative_position_bucket(
relative_position, # shape (query_length, key_length)
bidirectional=self.bidirectional_relative_attention,
num_buckets=self.num_relative_attention_buckets,
max_distance=self.max_relative_positions,
allow_negative_buckets=self.allow_negative_buckets,
)
return relative_position
def _compute_bias(self, query_length, key_length):
"""Compute binned relative position bias.
Args:
query_length (int): length of the query tensor.
key_length (int): length of the key tensor.
Returns:
values (Tensor): computed values for position bias.
"""
assert (
self.allow_negative_buckets is False
), "Cannot apply negative relative positions to embedding"
relative_position_bucket = self.compute_relative_positions(
query_length, key_length
)
# shape (query_length, key_length, num_heads)
values = self.relative_attention_bias(relative_position_bucket)
# shape (num_heads, query_length, key_length)
values = values.permute([2, 0, 1])
return values
def get_embedding(self):
return self.relative_attention_bias