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# 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 partial
import math
import torch
import torch.nn as nn
from typing import Optional, List, Tuple
from model.openfold.primitives import (
Linear,
LayerNorm,
Attention,
GlobalAttention,
_attention_chunked_trainable,
)
from onescience.utils.openfold.checkpointing import get_checkpoint_fn
from onescience.utils.openfold.chunk_utils import chunk_layer
from onescience.utils.openfold.tensor_utils import (
permute_final_dims,
flatten_final_dims,
)
class MSAAttention(nn.Module):
def __init__(
self,
c_in,
c_hidden,
no_heads,
pair_bias=False,
c_z=None,
inf=1e9,
):
"""
Args:
c_in:
Input channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
pair_bias:
Whether to use pair embedding bias
c_z:
Pair embedding channel dimension. Ignored unless pair_bias
is true
inf:
A large number to be used in computing the attention mask
"""
super(MSAAttention, self).__init__()
self.c_in = c_in
self.c_hidden = c_hidden
self.no_heads = no_heads
self.pair_bias = pair_bias
self.c_z = c_z
self.inf = inf
self.layer_norm_m = LayerNorm(self.c_in)
self.layer_norm_z = None
self.linear_z = None
if self.pair_bias:
self.layer_norm_z = LayerNorm(self.c_z)
self.linear_z = Linear(
self.c_z, 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,
m: torch.Tensor,
biases: Optional[List[torch.Tensor]],
chunk_size: int,
use_memory_efficient_kernel: bool,
use_deepspeed_evo_attention: bool,
use_lma: bool,
use_flash: bool,
flash_mask: Optional[torch.Tensor],
) -> torch.Tensor:
def fn(m, biases, flash_mask):
m = self.layer_norm_m(m)
return self.mha(
q_x=m,
kv_x=m,
biases=biases,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
use_flash=use_flash,
flash_mask=flash_mask,
)
inputs = {"m": m}
if(biases is not None):
inputs["biases"] = biases
else:
fn = partial(fn, biases=None)
if(use_flash and flash_mask is not None):
inputs["flash_mask"] = flash_mask
else:
fn = partial(fn, flash_mask=None)
return chunk_layer(
fn,
inputs,
chunk_size=chunk_size,
no_batch_dims=len(m.shape[:-2])
)
def _prep_inputs(self,
m: torch.Tensor,
z: Optional[torch.Tensor],
mask: Optional[torch.Tensor],
inplace_safe: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
n_seq, n_res = m.shape[-3:-1]
if mask is None:
# [*, N_seq, N_res]
mask = m.new_ones(
m.shape[:-3] + (n_seq, n_res),
)
# [*, N_seq, 1, 1, N_res]
mask_bias = (self.inf * (mask - 1))[..., :, None, None, :]
if (self.pair_bias and
z is not None and # For the
self.layer_norm_z is not None and # benefit of
self.linear_z is not None # TorchScript
):
chunks = []
for i in range(0, z.shape[-3], 256):
z_chunk = z[..., i: i + 256, :, :]
# [*, N_res, N_res, C_z]
z_chunk = self.layer_norm_z(z_chunk)
# [*, N_res, N_res, no_heads]
z_chunk = self.linear_z(z_chunk)
chunks.append(z_chunk)
z = torch.cat(chunks, dim=-3)
# [*, 1, no_heads, N_res, N_res]
z = permute_final_dims(z, (2, 0, 1)).unsqueeze(-4)
return m, mask_bias, z
@torch.jit.ignore
def _chunked_msa_attn(self,
m: torch.Tensor,
z: Optional[torch.Tensor],
mask: Optional[torch.Tensor],
chunk_logits: int,
checkpoint: bool,
inplace_safe: bool = False
) -> torch.Tensor:
"""
MSA attention with training-time chunking of the softmax computation.
Saves memory in the extra MSA stack. Probably obviated by our fused
attention kernel, which is now used by default.
"""
MSA_DIM = -4
def _get_qkv(m, z):
m, mask_bias, z = self._prep_inputs(
m, z, mask, inplace_safe=inplace_safe
)
m = self.layer_norm_m(m)
q, k, v = self.mha._prep_qkv(m, m)
return m, q, k, v, mask_bias, z
checkpoint_fn = get_checkpoint_fn()
if(torch.is_grad_enabled() and checkpoint):
m, q, k, v, mask_bias, z = checkpoint_fn(_get_qkv, m, z)
else:
m, q, k, v, mask_bias, z = _get_qkv(m, z)
o = _attention_chunked_trainable(
query=q,
key=k,
value=v,
biases=[mask_bias, z],
chunk_size=chunk_logits,
chunk_dim=MSA_DIM,
checkpoint=checkpoint,
)
if(torch.is_grad_enabled() and checkpoint):
# Storing an additional m here is far from ideal
m = checkpoint_fn(self.mha._wrap_up, o, m)
else:
m = self.mha._wrap_up(o, m)
return m
def forward(self,
m: torch.Tensor,
z: Optional[torch.Tensor] = None,
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,
use_flash: bool = False,
inplace_safe: bool = False,
_chunk_logits: Optional[int] = None,
_checkpoint_chunks: Optional[bool] = None,
) -> torch.Tensor:
"""
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
z:
[*, N_res, N_res, C_z] pair embedding. Required only if
pair_bias is True
mask:
[*, N_seq, N_res] MSA mask
chunk_size:
Size of chunks into which the inputs are split along their
batch dimensions. A low value decreases memory overhead at the
cost of slower execution. Chunking is not performed by default.
"""
if(_chunk_logits is not None):
return self._chunked_msa_attn(
m=m, z=z, mask=mask,
chunk_logits=_chunk_logits,
checkpoint=_checkpoint_chunks,
inplace_safe=inplace_safe,
)
if(use_flash):
assert z is None
biases = None
else:
m, mask_bias, z = self._prep_inputs(
m, z, mask, inplace_safe=inplace_safe
)
biases = [mask_bias]
if(z is not None):
biases.append(z)
if chunk_size is not None:
m = self._chunk(
m,
biases,
chunk_size,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
use_flash=use_flash,
flash_mask=mask,
)
else:
m = self.layer_norm_m(m)
m = self.mha(
q_x=m,
kv_x=m,
biases=biases,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
use_flash=use_flash,
flash_mask=mask,
)
return m
class MSARowAttentionWithPairBias(MSAAttention):
"""
Implements Algorithm 7.
"""
def __init__(self, c_m, c_z, c_hidden, no_heads, inf=1e9):
"""
Args:
c_m:
Input channel dimension
c_z:
Pair embedding channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
inf:
Large number used to construct attention masks
"""
super(MSARowAttentionWithPairBias, self).__init__(
c_m,
c_hidden,
no_heads,
pair_bias=True,
c_z=c_z,
inf=inf,
)
class MSAColumnAttention(nn.Module):
"""
Implements Algorithm 8.
By rights, this should also be a subclass of MSAAttention. Alas,
most inheritance isn't supported by TorchScript.
"""
def __init__(self, c_m, c_hidden, no_heads, inf=1e9):
"""
Args:
c_m:
MSA channel dimension
c_hidden:
Per-head hidden channel dimension
no_heads:
Number of attention heads
inf:
Large number used to construct attention masks
"""
super(MSAColumnAttention, self).__init__()
self.c_m = c_m
self.c_hidden = c_hidden
self.no_heads = no_heads
self.inf = inf
self._msa_att = MSAAttention(
c_in=c_m,
c_hidden=c_hidden,
no_heads=no_heads,
pair_bias=False,
c_z=None,
inf=inf,
)
def forward(self,
m: torch.Tensor,
mask: Optional[torch.Tensor] = None,
chunk_size: Optional[int] = None,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
use_flash: bool = False,
) -> torch.Tensor:
"""
Args:
m:
[*, N_seq, N_res, C_m] MSA embedding
mask:
[*, N_seq, N_res] MSA mask
chunk_size:
Size of chunks into which the inputs are split along their
batch dimensions. A low value decreases memory overhead at the
cost of slower execution. Chunking is not performed by default.
"""
# [*, N_res, N_seq, C_in]
m = m.transpose(-2, -3)
if mask is not None:
mask = mask.transpose(-1, -2)
m = self._msa_att(
m,
mask=mask,
chunk_size=chunk_size,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
use_flash=use_flash,
)
# [*, N_seq, N_res, C_in]
m = m.transpose(-2, -3)
if mask is not None:
mask = mask.transpose(-1, -2)
return m
class MSAColumnGlobalAttention(nn.Module):
def __init__(
self, c_in, c_hidden, no_heads, inf=1e9, eps=1e-10,
):
super(MSAColumnGlobalAttention, self).__init__()
self.c_in = c_in
self.c_hidden = c_hidden
self.no_heads = no_heads
self.inf = inf
self.eps = eps
self.layer_norm_m = nn.LayerNorm(c_in)
self.global_attention = GlobalAttention(
c_in=c_in,
c_hidden=c_hidden,
no_heads=no_heads,
inf=inf,
eps=eps,
)
@torch.jit.ignore
def _chunk(self,
m: torch.Tensor,
mask: torch.Tensor,
chunk_size: int,
use_lma: bool = False,
) -> torch.Tensor:
mha_input = {
"m": m,
"mask": mask,
}
def fn(m, mask):
m = self.layer_norm_m(m)
return self.global_attention(m, mask, use_lma=use_lma)
return chunk_layer(
fn,
mha_input,
chunk_size=chunk_size,
no_batch_dims=len(m.shape[:-2]),
)
def forward(
self,
m: torch.Tensor,
mask: Optional[torch.Tensor] = None,
chunk_size: Optional[int] = None,
use_lma: bool = False,
) -> torch.Tensor:
n_seq, n_res, c_in = m.shape[-3:]
if mask is None:
# [*, N_seq, N_res]
mask = torch.ones(
m.shape[:-1],
dtype=m.dtype,
device=m.device,
).detach()
# [*, N_res, N_seq, C_in]
m = m.transpose(-2, -3)
mask = mask.transpose(-1, -2)
if chunk_size is not None:
m = self._chunk(m, mask, chunk_size, use_lma=use_lma)
else:
m = self.layer_norm_m(m)
m = self.global_attention(m=m, mask=mask, use_lma=use_lma)
# [*, N_seq, N_res, C_in]
m = m.transpose(-2, -3)
return m
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