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# Copyright 2024 ByteDance and/or its affiliates.
#
# 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.
# pylint: disable=C0114
from functools import partial
from typing import Any, Optional, Union
import torch
import torch.nn as nn
from protenix.model.modules.primitives import LinearNoBias, Transition
from protenix.model.modules.transformer import AttentionPairBias
from protenix.model.utils import sample_msa_feature_dict_random_without_replacement
from protenix.openfold_local.model.dropout import DropoutRowwise
from protenix.openfold_local.model.outer_product_mean import (
OuterProductMean, # Alg 9 in AF3
)
from protenix.openfold_local.model.primitives import LayerNorm
from protenix.openfold_local.model.triangular_attention import TriangleAttention
from protenix.openfold_local.model.triangular_multiplicative_update import (
TriangleMultiplicationIncoming, # Alg 13 in AF3
)
from protenix.openfold_local.model.triangular_multiplicative_update import (
TriangleMultiplicationOutgoing, # Alg 12 in AF3
)
from protenix.openfold_local.utils.checkpointing import checkpoint_blocks
class PairformerBlock(nn.Module):
"""Implements Algorithm 17 [Line2-Line8] in AF3
c_hidden_mul is set as openfold
Ref to:
https://github.com/aqlaboratory/openfold/blob/feb45a521e11af1db241a33d58fb175e207f8ce0/openfold/model/evoformer.py#L123
"""
def __init__(
self,
n_heads: int = 16,
c_z: int = 128,
c_s: int = 384,
c_hidden_mul: int = 128,
c_hidden_pair_att: int = 32,
no_heads_pair: int = 4,
dropout: float = 0.25,
) -> None:
"""
Args:
n_heads (int, optional): number of head [for AttentionPairBias]. Defaults to 16.
c_z (int, optional): hidden dim [for pair embedding]. Defaults to 128.
c_s (int, optional): hidden dim [for single embedding]. Defaults to 384.
c_hidden_mul (int, optional): hidden dim [for TriangleMultiplicationOutgoing].
Defaults to 128.
c_hidden_pair_att (int, optional): hidden dim [for TriangleAttention]. Defaults to 32.
no_heads_pair (int, optional): number of head [for TriangleAttention]. Defaults to 4.
dropout (float, optional): dropout ratio [for TriangleUpdate]. Defaults to 0.25.
"""
super(PairformerBlock, self).__init__()
self.n_heads = n_heads
self.tri_mul_out = TriangleMultiplicationOutgoing(
c_z=c_z, c_hidden=c_hidden_mul
)
self.tri_mul_in = TriangleMultiplicationIncoming(c_z=c_z, c_hidden=c_hidden_mul)
self.tri_att_start = TriangleAttention(
c_in=c_z,
c_hidden=c_hidden_pair_att,
no_heads=no_heads_pair,
)
self.tri_att_end = TriangleAttention(
c_in=c_z,
c_hidden=c_hidden_pair_att,
no_heads=no_heads_pair,
)
self.dropout_row = DropoutRowwise(dropout)
self.pair_transition = Transition(c_in=c_z, n=4)
self.c_s = c_s
if self.c_s > 0:
self.attention_pair_bias = AttentionPairBias(
has_s=False, n_heads=n_heads, c_a=c_s, c_z=c_z
)
self.single_transition = Transition(c_in=c_s, n=4)
def forward(
self,
s: Optional[torch.Tensor],
z: torch.Tensor,
pair_mask: torch.Tensor,
use_memory_efficient_kernel: bool = False,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
) -> tuple[Optional[torch.Tensor], torch.Tensor]:
"""
Forward pass of the PairformerBlock.
Args:
s (Optional[torch.Tensor]): single feature
[..., N_token, c_s]
z (torch.Tensor): pair embedding
[..., N_token, N_token, c_z]
pair_mask (torch.Tensor): pair mask
[..., N_token, N_token]
use_memory_efficient_kernel (bool): Whether to use memory-efficient kernel. Defaults to False.
use_deepspeed_evo_attention (bool): Whether to use DeepSpeed evolutionary attention. Defaults to False.
use_lma (bool): Whether to use low-memory attention. Defaults to False.
inplace_safe (bool): Whether it is safe to use inplace operations. Defaults to False.
chunk_size (Optional[int]): Chunk size for memory-efficient operations. Defaults to None.
Returns:
tuple[Optional[torch.Tensor], torch.Tensor]: the update of s[Optional] and z
[..., N_token, c_s] | None
[..., N_token, N_token, c_z]
"""
if inplace_safe:
z = self.tri_mul_out(
z, mask=pair_mask, inplace_safe=inplace_safe, _add_with_inplace=True
)
z = self.tri_mul_in(
z, mask=pair_mask, inplace_safe=inplace_safe, _add_with_inplace=True
)
z += self.tri_att_start(
z,
mask=pair_mask,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
)
z = z.transpose(-2, -3).contiguous()
z += self.tri_att_end(
z,
mask=pair_mask.tranpose(-1, -2) if pair_mask is not None else None,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
)
z = z.transpose(-2, -3).contiguous()
z += self.pair_transition(z)
if self.c_s > 0:
s += self.attention_pair_bias(
a=s,
s=None,
z=z,
)
s += self.single_transition(s)
return s, z
else:
tmu_update = self.tri_mul_out(
z, mask=pair_mask, inplace_safe=inplace_safe, _add_with_inplace=False
)
z = z + self.dropout_row(tmu_update)
del tmu_update
tmu_update = self.tri_mul_in(
z, mask=pair_mask, inplace_safe=inplace_safe, _add_with_inplace=False
)
z = z + self.dropout_row(tmu_update)
del tmu_update
z = z + self.dropout_row(
self.tri_att_start(
z,
mask=pair_mask,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
)
)
z = z.transpose(-2, -3)
z = z + self.dropout_row(
self.tri_att_end(
z,
mask=pair_mask.tranpose(-1, -2) if pair_mask is not None else None,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
)
)
z = z.transpose(-2, -3)
z = z + self.pair_transition(z)
if self.c_s > 0:
s = s + self.attention_pair_bias(
a=s,
s=None,
z=z,
)
s = s + self.single_transition(s)
return s, z
class PairformerStack(nn.Module):
"""
Implements Algorithm 17 [PairformerStack] in AF3
"""
def __init__(
self,
n_blocks: int = 48,
n_heads: int = 16,
c_z: int = 128,
c_s: int = 384,
dropout: float = 0.25,
blocks_per_ckpt: Optional[int] = None,
) -> None:
"""
Args:
n_blocks (int, optional): number of blocks [for PairformerStack]. Defaults to 48.
n_heads (int, optional): number of head [for AttentionPairBias]. Defaults to 16.
c_z (int, optional): hidden dim [for pair embedding]. Defaults to 128.
c_s (int, optional): hidden dim [for single embedding]. Defaults to 384.
dropout (float, optional): dropout ratio. Defaults to 0.25.
blocks_per_ckpt: number of Pairformer blocks in each activation checkpoint
Size of each chunk. A higher value corresponds to fewer
checkpoints, and trades memory for speed. If None, no checkpointing
is performed.
"""
super(PairformerStack, self).__init__()
self.n_blocks = n_blocks
self.n_heads = n_heads
self.blocks_per_ckpt = blocks_per_ckpt
self.blocks = nn.ModuleList()
for _ in range(n_blocks):
block = PairformerBlock(n_heads=n_heads, c_z=c_z, c_s=c_s, dropout=dropout)
self.blocks.append(block)
def _prep_blocks(
self,
pair_mask: Optional[torch.Tensor],
use_memory_efficient_kernel: bool = False,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
clear_cache_between_blocks: bool = False,
):
blocks = [
partial(
b,
pair_mask=pair_mask,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
)
for b in self.blocks
]
def clear_cache(b, *args, **kwargs):
torch.cuda.empty_cache()
return b(*args, **kwargs)
if clear_cache_between_blocks:
blocks = [partial(clear_cache, b) for b in blocks]
return blocks
def forward(
self,
s: torch.Tensor,
z: torch.Tensor,
pair_mask: torch.Tensor,
use_memory_efficient_kernel: bool = False,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Args:
s (Optional[torch.Tensor]): single feature
[..., N_token, c_s]
z (torch.Tensor): pair embedding
[..., N_token, N_token, c_z]
pair_mask (torch.Tensor): pair mask
[..., N_token, N_token]
use_memory_efficient_kernel (bool): Whether to use memory-efficient kernel. Defaults to False.
use_deepspeed_evo_attention (bool): Whether to use DeepSpeed evolutionary attention. Defaults to False.
use_lma (bool): Whether to use low-memory attention. Defaults to False.
inplace_safe (bool): Whether it is safe to use inplace operations. Defaults to False.
chunk_size (Optional[int]): Chunk size for memory-efficient operations. Defaults to None.
Returns:
tuple[torch.Tensor, torch.Tensor]: the update of s and z
[..., N_token, c_s]
[..., N_token, N_token, c_z]
"""
if z.shape[-2] > 2000 and (not self.training):
clear_cache_between_blocks = True
else:
clear_cache_between_blocks = False
blocks = self._prep_blocks(
pair_mask=pair_mask,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
clear_cache_between_blocks=clear_cache_between_blocks,
)
blocks_per_ckpt = self.blocks_per_ckpt
if not torch.is_grad_enabled():
blocks_per_ckpt = None
s, z = checkpoint_blocks(
blocks,
args=(s, z),
blocks_per_ckpt=blocks_per_ckpt,
)
return s, z
class MSAPairWeightedAveraging(nn.Module):
"""
Implements Algorithm 10 [MSAPairWeightedAveraging] in AF3
"""
def __init__(self, c_m: int = 64, c: int = 32, c_z: int = 128, n_heads=8) -> None:
"""
Args:
c_m (int, optional): hidden dim [for msa embedding]. Defaults to 64.
c (int, optional): hidden [for MSAPairWeightedAveraging] dim. Defaults to 32.
c_z (int, optional): hidden dim [for pair embedding]. Defaults to 128.
n_heads (int, optional): number of heads [for MSAPairWeightedAveraging]. Defaults to 8.
"""
super(MSAPairWeightedAveraging, self).__init__()
self.c_m = c_m
self.c = c
self.n_heads = n_heads
self.c_z = c_z
# Input projections
self.layernorm_m = LayerNorm(self.c_m)
self.linear_no_bias_mv = LinearNoBias(
in_features=self.c_m, out_features=self.c * self.n_heads
)
self.layernorm_z = LayerNorm(self.c_z)
self.linear_no_bias_z = LinearNoBias(
in_features=self.c_z, out_features=self.n_heads
)
self.linear_no_bias_mg = LinearNoBias(
in_features=self.c_m, out_features=self.c * self.n_heads
)
# Weighted average with gating
self.softmax_w = nn.Softmax(dim=-2)
# Output projection
self.linear_no_bias_out = LinearNoBias(
in_features=self.c * self.n_heads, out_features=self.c_m
)
def forward(self, m: torch.Tensor, z: torch.Tensor) -> torch.Tensor:
"""
Args:
m (torch.Tensor): msa embedding
[...,n_msa_sampled, n_token, c_m]
z (torch.Tensor): pair embedding
[...,n_token, n_token, c_z]
Returns:
torch.Tensor: updated msa embedding
[...,n_msa_sampled, n_token, c_m]
"""
# Input projections
m = self.layernorm_m(m) # [...,n_msa_sampled, n_token, c_m]
v = self.linear_no_bias_mv(m) # [...,n_msa_sampled, n_token, n_heads * c]
v = v.reshape(
*v.shape[:-1], self.n_heads, self.c
) # [...,n_msa_sampled, n_token, n_heads, c]
b = self.linear_no_bias_z(
self.layernorm_z(z)
) # [...,n_token, n_token, n_heads]
g = torch.sigmoid(
self.linear_no_bias_mg(m)
) # [...,n_msa_sampled, n_token, n_heads * c]
g = g.reshape(
*g.shape[:-1], self.n_heads, self.c
) # [...,n_msa_sampled, n_token, n_heads, c]
w = self.softmax_w(b) # [...,n_token, n_token, n_heads]
wv = torch.einsum(
"...ijh,...mjhc->...mihc", w, v
) # [...,n_msa_sampled,n_token,n_heads,c]
o = g * wv
o = o.reshape(
*o.shape[:-2], self.n_heads * self.c
) # [...,n_msa_sampled, n_token, n_heads * c]
m = self.linear_no_bias_out(o) # [...,n_msa_sampled, n_token, c_m]
return m
class MSAStack(nn.Module):
"""
Implements MSAStack Line7-Line8 in Algorithm 8
"""
def __init__(self, c_m: int = 64, c: int = 8, dropout: float = 0.15) -> None:
"""
Args:
c_m (int, optional): hidden dim [for msa embedding]. Defaults to 64.
c (int, optional): hidden [for MSAStack] dim. Defaults to 8.
dropout (float, optional): dropout ratio. Defaults to 0.15.
"""
super(MSAStack, self).__init__()
self.c = c
self.msa_pair_weighted_averaging = MSAPairWeightedAveraging(c=self.c)
self.dropout_row = DropoutRowwise(dropout)
self.transition_m = Transition(c_in=c_m, n=4)
def forward(self, m: torch.Tensor, z: torch.Tensor) -> torch.Tensor:
"""
Args:
m (torch.Tensor): msa embedding
[...,n_msa_sampled, n_token, c_m]
z (torch.Tensor): pair embedding
[...,n_token, n_token, c_z]
Returns:
torch.Tensor: updated msa embedding
[...,n_msa_sampled, n_token, c_m]
"""
m = m + self.dropout_row(self.msa_pair_weighted_averaging(m, z))
m = m + self.transition_m(m)
return m
class MSABlock(nn.Module):
"""
Base MSA Block, Line6-Line13 in Algorithm 8
"""
def __init__(
self,
c_m: int = 64,
c_z: int = 128,
c_hidden: int = 32,
is_last_block: bool = False,
msa_dropout: float = 0.15,
pair_dropout: float = 0.25,
) -> None:
"""
Args:
c_m (int, optional): hidden dim [for msa embedding]. Defaults to 64.
c_z (int, optional): hidden dim [for pair embedding]. Defaults to 128.
c_hidden (int, optional): hidden dim [for MSABlock]. Defaults to 32.
is_last_block (int): if this is the last block of MSAModule. Defaults to False.
msa_dropout (float, optional): dropout ratio for msa block. Defaults to 0.15.
pair_dropout (float, optional): dropout ratio for pair stack. Defaults to 0.25.
"""
super(MSABlock, self).__init__()
self.c_m = c_m
self.c_z = c_z
self.c_hidden = c_hidden
self.is_last_block = is_last_block
# Communication
self.outer_product_mean_msa = OuterProductMean(
c_m=self.c_m, c_z=self.c_z, c_hidden=self.c_hidden
)
if not self.is_last_block:
# MSA stack
self.msa_stack = MSAStack(c_m=self.c_m, dropout=msa_dropout)
# Pair stack
self.pair_stack = PairformerBlock(c_z=c_z, c_s=0, dropout=pair_dropout)
def forward(
self,
m: torch.Tensor,
z: torch.Tensor,
pair_mask,
use_memory_efficient_kernel: bool = False,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Args:
m (torch.Tensor): msa embedding
[...,n_msa_sampled, n_token, c_m]
z (torch.Tensor): pair embedding
[...,n_token, n_token, c_z]
pair_mask (torch.Tensor): pair mask
[..., N_token, N_token]
use_memory_efficient_kernel (bool): Whether to use memory-efficient kernel. Defaults to False.
use_deepspeed_evo_attention (bool): Whether to use DeepSpeed evolutionary attention. Defaults to False.
use_lma (bool): Whether to use low-memory attention. Defaults to False.
inplace_safe (bool): Whether it is safe to use inplace operations. Defaults to False.
chunk_size (Optional[int]): Chunk size for memory-efficient operations. Defaults to None.
Returns:
tuple[torch.Tensor, torch.Tensor]: updated m z of MSABlock
[...,n_msa_sampled, n_token, c_m]
[...,n_token, n_token, c_z]
"""
# Communication
z = z + self.outer_product_mean_msa(
m, inplace_safe=inplace_safe, chunk_size=chunk_size
)
if not self.is_last_block:
# MSA stack
m = self.msa_stack(m, z)
# Pair stack
_, z = self.pair_stack(
s=None,
z=z,
pair_mask=pair_mask,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
)
if not self.is_last_block:
return m, z
else:
return None, z # to ensure that `m` will not be used.
class MSAModule(nn.Module):
"""
Implements Algorithm 8 [MSAModule] in AF3
"""
def __init__(
self,
n_blocks: int = 4,
c_m: int = 64,
c_z: int = 128,
c_s_inputs: int = 449,
msa_dropout: float = 0.15,
pair_dropout: float = 0.25,
blocks_per_ckpt: Optional[int] = 1,
msa_configs: dict = None,
) -> None:
"""Main Entry of MSAModule
Args:
n_blocks (int, optional): number of blocks [for MSAModule]. Defaults to 4.
c_m (int, optional): hidden dim [for msa embedding]. Defaults to 64.
c_z (int, optional): hidden dim [for pair embedding]. Defaults to 128.
c_s_inputs (int, optional):
hidden dim for single embedding from InputFeatureEmbedder. Defaults to 449.
msa_dropout (float, optional): dropout ratio for msa block. Defaults to 0.15.
pair_dropout (float, optional): dropout ratio for pair stack. Defaults to 0.25.
blocks_per_ckpt: number of MSAModule blocks in each activation checkpoint
Size of each chunk. A higher value corresponds to fewer
checkpoints, and trades memory for speed. If None, no checkpointing
is performed.
msa_configs (dict, optional): a dictionary containing keys:
"enable": whether using msa embedding.
]"""
super(MSAModule, self).__init__()
self.n_blocks = n_blocks
self.c_m = c_m
self.c_s_inputs = c_s_inputs
self.blocks_per_ckpt = blocks_per_ckpt
self.input_feature = {
"msa": 32,
"has_deletion": 1,
"deletion_value": 1,
}
self.msa_configs = {
"enable": msa_configs.get("enable", False),
"strategy": msa_configs.get("strategy", "random"),
}
if "sample_cutoff" in msa_configs:
self.msa_configs["train_cutoff"] = msa_configs["sample_cutoff"].get(
"train", 512
)
self.msa_configs["test_cutoff"] = msa_configs["sample_cutoff"].get(
"test", 16384
)
if "min_size" in msa_configs:
self.msa_configs["train_lowerb"] = msa_configs["min_size"].get("train", 1)
self.msa_configs["test_lowerb"] = msa_configs["min_size"].get("test", 1)
self.linear_no_bias_m = LinearNoBias(
in_features=32 + 1 + 1, out_features=self.c_m
)
self.linear_no_bias_s = LinearNoBias(
in_features=self.c_s_inputs, out_features=self.c_m
)
self.blocks = nn.ModuleList()
for i in range(n_blocks):
block = MSABlock(
c_m=self.c_m,
c_z=c_z,
is_last_block=(i + 1 == n_blocks),
msa_dropout=msa_dropout,
pair_dropout=pair_dropout,
)
self.blocks.append(block)
def _prep_blocks(
self,
pair_mask: Optional[torch.Tensor],
use_memory_efficient_kernel: bool = False,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
clear_cache_between_blocks: bool = False,
):
blocks = [
partial(
b,
pair_mask=pair_mask,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
)
for b in self.blocks
]
def clear_cache(b, *args, **kwargs):
torch.cuda.empty_cache()
return b(*args, **kwargs)
if clear_cache_between_blocks:
blocks = [partial(clear_cache, b) for b in blocks]
return blocks
def forward(
self,
input_feature_dict: dict[str, Any],
z: torch.Tensor,
s_inputs: torch.Tensor,
pair_mask: torch.Tensor,
use_memory_efficient_kernel: bool = False,
use_deepspeed_evo_attention: bool = False,
use_lma: bool = False,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
) -> torch.Tensor:
"""
Args:
input_feature_dict (dict[str, Any]):
input meta feature dict
z (torch.Tensor): pair embedding
[..., N_token, N_token, c_z]
s_inputs (torch.Tensor): single embedding from InputFeatureEmbedder
[..., N_token, c_s_inputs]
pair_mask (torch.Tensor): pair mask
[..., N_token, N_token]
use_memory_efficient_kernel (bool): Whether to use memory-efficient kernel. Defaults to False.
use_deepspeed_evo_attention (bool): Whether to use DeepSpeed evolutionary attention. Defaults to False.
use_lma (bool): Whether to use low-memory attention. Defaults to False.
inplace_safe (bool): Whether it is safe to use inplace operations. Defaults to False.
chunk_size (Optional[int]): Chunk size for memory-efficient operations. Defaults to None.
Returns:
torch.Tensor: the updated z
[..., N_token, N_token, c_z]
"""
# If n_blocks < 1, return z
if self.n_blocks < 1:
return z
if "msa" not in input_feature_dict:
return z
msa_feat = sample_msa_feature_dict_random_without_replacement(
feat_dict=input_feature_dict,
dim_dict={feat_name: -2 for feat_name in self.input_feature},
cutoff=(
self.msa_configs["train_cutoff"]
if self.training
else self.msa_configs["test_cutoff"]
),
lower_bound=(
self.msa_configs["train_lowerb"]
if self.training
else self.msa_configs["test_lowerb"]
),
strategy=self.msa_configs["strategy"],
)
# pylint: disable=E1102
msa_feat["msa"] = torch.nn.functional.one_hot(
msa_feat["msa"],
num_classes=self.input_feature["msa"],
)
target_shape = msa_feat["msa"].shape[:-1]
msa_sample = torch.cat(
[
msa_feat[name].reshape(*target_shape, d)
for name, d in self.input_feature.items()
],
dim=-1,
) # [..., N_msa_sample, N_token, 32 + 1 + 1]
# Line2
msa_sample = self.linear_no_bias_m(msa_sample)
# Auto broadcast [...,n_msa_sampled, n_token, c_m]
msa_sample = msa_sample + self.linear_no_bias_s(s_inputs)
if z.shape[-2] > 2000 and (not self.training):
clear_cache_between_blocks = True
else:
clear_cache_between_blocks = False
blocks = self._prep_blocks(
pair_mask=pair_mask,
use_memory_efficient_kernel=use_memory_efficient_kernel,
use_deepspeed_evo_attention=use_deepspeed_evo_attention,
use_lma=use_lma,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
clear_cache_between_blocks=clear_cache_between_blocks,
)
blocks_per_ckpt = self.blocks_per_ckpt
if not torch.is_grad_enabled():
blocks_per_ckpt = None
msa_sample, z = checkpoint_blocks(
blocks,
args=(msa_sample, z),
blocks_per_ckpt=blocks_per_ckpt,
)
if z.shape[-2] > 2000:
torch.cuda.empty_cache()
return z
class TemplateEmbedder(nn.Module):
"""
Implements Algorithm 16 in AF3
"""
def __init__(
self,
n_blocks: int = 2,
c: int = 64,
c_z: int = 128,
dropout: float = 0.25,
blocks_per_ckpt: Optional[int] = None,
) -> None:
"""
Args:
n_blocks (int, optional): number of blocks for TemplateEmbedder. Defaults to 2.
c (int, optional): hidden dim of TemplateEmbedder. Defaults to 64.
c_z (int, optional): hidden dim [for pair embedding]. Defaults to 128.
dropout (float, optional): dropout ratio for PairformerStack. Defaults to 0.25.
Note this value is missed in Algorithm 16, so we use default ratio for Pairformer
blocks_per_ckpt: number of TemplateEmbedder/Pairformer blocks in each activation
checkpoint Size of each chunk. A higher value corresponds to fewer
checkpoints, and trades memory for speed. If None, no checkpointing
is performed.
"""
super(TemplateEmbedder, self).__init__()
self.n_blocks = n_blocks
self.c = c
self.c_z = c_z
self.input_feature1 = {
"template_distogram": 39,
"b_template_backbone_frame_mask": 1,
"template_unit_vector": 3,
"b_template_pseudo_beta_mask": 1,
}
self.input_feature2 = {
"template_restype_i": 32,
"template_restype_j": 32,
}
self.distogram = {"max_bin": 50.75, "min_bin": 3.25, "no_bins": 39}
self.inf = 100000.0
self.linear_no_bias_z = LinearNoBias(in_features=self.c_z, out_features=self.c)
self.layernorm_z = LayerNorm(self.c_z)
self.linear_no_bias_a = LinearNoBias(
in_features=sum(self.input_feature1.values())
+ sum(self.input_feature2.values()),
out_features=self.c,
)
self.pairformer_stack = PairformerStack(
c_s=0,
c_z=c,
n_blocks=self.n_blocks,
dropout=dropout,
blocks_per_ckpt=blocks_per_ckpt,
)
self.layernorm_v = LayerNorm(self.c)
self.linear_no_bias_u = LinearNoBias(in_features=self.c, out_features=self.c_z)
def forward(
self,
input_feature_dict: dict[str, Any],
z: torch.Tensor, # pylint: disable=W0613
pair_mask: torch.Tensor = None, # pylint: disable=W0613
use_memory_efficient_kernel: bool = False, # pylint: disable=W0613
use_deepspeed_evo_attention: bool = False, # pylint: disable=W0613
use_lma: bool = False, # pylint: disable=W0613
inplace_safe: bool = False, # pylint: disable=W0613
chunk_size: Optional[int] = None, # pylint: disable=W0613
) -> torch.Tensor:
"""
Args:
input_feature_dict (dict[str, Any]): input feature dict
z (torch.Tensor): pair embedding
[..., N_token, N_token, c_z]
pair_mask (torch.Tensor, optional): pair masking. Default to None.
[..., N_token, N_token]
Returns:
torch.Tensor: the template feature
[..., N_token, N_token, c_z]
"""
# In this version, we do not use TemplateEmbedder by setting n_blocks=0
if "template_restype" not in input_feature_dict or self.n_blocks < 1:
return 0
return 0
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