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triangular_attention.py
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triangular_attention.py
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# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
from functools import partialmethod, partial
import math
from typing import Optional, List
import torch
import torch.nn as nn
from openfold.model.primitives import Linear, LayerNorm, Attention
from openfold.utils.chunk_utils import chunk_layer
from openfold.utils.tensor_utils import (
permute_final_dims,
flatten_final_dims,
)
class TriangleAttention(nn.Module):
def __init__(
self, c_in, c_hidden, no_heads, starting=True, inf=1e9
):
"""
Args:
c_in:
Input channel dimension
c_hidden:
Overall hidden channel dimension (not per-head)
no_heads:
Number of attention heads
"""
super(TriangleAttention, self).__init__()
self.c_in = c_in
self.c_hidden = c_hidden
self.no_heads = no_heads
self.starting = starting
self.inf = inf
self.layer_norm = LayerNorm(self.c_in)
self.linear = Linear(c_in, self.no_heads, bias=False, init="normal")
self.mha = Attention(
self.c_in, self.c_in, self.c_in, self.c_hidden, self.no_heads
)
@torch.jit.ignore
def _chunk(self,
x: torch.Tensor,
biases: List[torch.Tensor],
chunk_size: int,
use_memory_efficient_kernel: bool = False,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
) -> torch.Tensor:
"triangle! triangle!"
mha_inputs = {
"q_x": x,
"kv_x": x,
"biases": biases,
}
return chunk_layer(
partial(
self.mha,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma
),
mha_inputs,
chunk_size=chunk_size,
no_batch_dims=len(x.shape[:-2]),
_out=x if inplace_safe else None,
)
def forward(self,
x: torch.Tensor,
mask: Optional[torch.Tensor] = None,
chunk_size: Optional[int] = None,
use_memory_efficient_kernel: bool = False,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
) -> torch.Tensor:
"""
Args:
x:
[*, I, J, C_in] input tensor (e.g. the pair representation)
Returns:
[*, I, J, C_in] output tensor
"""
if mask is None:
# [*, I, J]
mask = x.new_ones(
x.shape[:-1],
)
if(not self.starting):
x = x.transpose(-2, -3)
mask = mask.transpose(-1, -2)
# [*, I, J, C_in]
x = self.layer_norm(x)
# [*, I, 1, 1, J]
mask_bias = (self.inf * (mask - 1))[..., :, None, None, :]
# [*, H, I, J]
triangle_bias = permute_final_dims(self.linear(x), (2, 0, 1))
# [*, 1, H, I, J]
triangle_bias = triangle_bias.unsqueeze(-4)
biases = [mask_bias, triangle_bias]
if chunk_size is not None:
x = self._chunk(
x,
biases,
chunk_size,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
)
else:
x = self.mha(
q_x=x,
kv_x=x,
biases=biases,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma
)
if(not self.starting):
x = x.transpose(-2, -3)
return x
# Implements Algorithm 13
TriangleAttentionStartingNode = TriangleAttention
class TriangleAttentionEndingNode(TriangleAttention):
"""
Implements Algorithm 14.
"""
__init__ = partialmethod(TriangleAttention.__init__, starting=False)