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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.
from typing import Optional, Union
import torch
import torch.nn as nn
from protenix.model.modules.pairformer import PairformerStack
from protenix.model.modules.primitives import LinearNoBias
from protenix.model.utils import broadcast_token_to_atom, one_hot
from protenix.openfold_local.model.primitives import LayerNorm
from protenix.utils.torch_utils import cdist
class ConfidenceHead(nn.Module):
"""
Implements Algorithm 31 in AF3
"""
def __init__(
self,
n_blocks: int = 4,
c_s: int = 384,
c_z: int = 128,
c_s_inputs: int = 449,
b_pae: int = 64,
b_pde: int = 64,
b_plddt: int = 50,
b_resolved: int = 2,
max_atoms_per_token: int = 20,
pairformer_dropout: float = 0.0,
blocks_per_ckpt: Optional[int] = None,
distance_bin_start: float = 3.25,
distance_bin_end: float = 52.0,
distance_bin_step: float = 1.25,
stop_gradient: bool = True,
) -> None:
"""
Args:
n_blocks (int, optional): number of blocks for ConfidenceHead. Defaults to 4.
c_s (int, optional): hidden dim [for single embedding]. Defaults to 384.
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.
b_pae (int, optional): the bin number for pae. Defaults to 64.
b_pde (int, optional): the bin numer for pde. Defaults to 64.
b_plddt (int, optional): the bin number for plddt. Defaults to 50.
b_resolved (int, optional): the bin number for resolved. Defaults to 2.
max_atoms_per_token (int, optional): max atoms in a token. Defaults to 20.
pairformer_dropout (float, optional): dropout ratio for Pairformer. Defaults to 0.0.
blocks_per_ckpt: number of Pairformer blocks in each activation checkpoint
distance_bin_start (float, optional): Start of the distance bin range. Defaults to 3.375.
distance_bin_end (float, optional): End of the distance bin range. Defaults to 21.375.
distance_bin_step (float, optional): Step size for the distance bins. Defaults to 1.25.
stop_gradient (bool, optional): Whether to stop gradient propagation. Defaults to True.
"""
super(ConfidenceHead, self).__init__()
self.n_blocks = n_blocks
self.c_s = c_s
self.c_z = c_z
self.c_s_inputs = c_s_inputs
self.b_pae = b_pae
self.b_pde = b_pde
self.b_plddt = b_plddt
self.b_resolved = b_resolved
self.max_atoms_per_token = max_atoms_per_token
self.stop_gradient = stop_gradient
self.linear_no_bias_s1 = LinearNoBias(
in_features=self.c_s_inputs, out_features=self.c_z
)
self.linear_no_bias_s2 = LinearNoBias(
in_features=self.c_s_inputs, out_features=self.c_z
)
lower_bins = torch.arange(
distance_bin_start, distance_bin_end, distance_bin_step
)
upper_bins = torch.cat([lower_bins[1:], torch.tensor([1e6])])
self.lower_bins = nn.Parameter(lower_bins, requires_grad=False)
self.upper_bins = nn.Parameter(upper_bins, requires_grad=False)
self.num_bins = len(lower_bins) # + 1
self.linear_no_bias_d = LinearNoBias(
in_features=self.num_bins, out_features=self.c_z
)
self.pairformer_stack = PairformerStack(
c_z=self.c_z,
c_s=self.c_s,
n_blocks=n_blocks,
dropout=pairformer_dropout,
blocks_per_ckpt=blocks_per_ckpt,
)
self.linear_no_bias_pae = LinearNoBias(
in_features=self.c_z, out_features=self.b_pae
)
self.linear_no_bias_pde = LinearNoBias(
in_features=self.c_z, out_features=self.b_pde
)
self.plddt_weight = nn.Parameter(
data=torch.empty(size=(self.max_atoms_per_token, self.c_s, self.b_plddt))
)
self.resolved_weight = nn.Parameter(
data=torch.empty(size=(self.max_atoms_per_token, self.c_s, self.b_resolved))
)
self.linear_no_bias_s_inputs = LinearNoBias(self.c_s_inputs, self.c_s)
self.linear_no_bias_s_trunk = LinearNoBias(self.c_s, self.c_s)
self.layernorm_s_trunk = LayerNorm(self.c_s)
self.linear_no_bias_z_trunk = LinearNoBias(self.c_z, self.c_z)
self.layernorm_z_trunk = LayerNorm(self.c_z)
self.layernorm_no_bias_z_cat = nn.LayerNorm(self.c_z * 2, bias=False)
self.layernorm_no_bias_s_cat = nn.LayerNorm(self.c_s * 2, bias=False)
self.linear_no_bias_z_cat = LinearNoBias(self.c_z * 2, self.c_z)
self.linear_no_bias_s_cat = LinearNoBias(self.c_s * 2, self.c_s)
# Output layernorm
self.pae_ln = LayerNorm(self.c_z)
self.pde_ln = LayerNorm(self.c_z)
self.plddt_ln = LayerNorm(self.c_s)
self.resolved_ln = LayerNorm(self.c_s)
with torch.no_grad():
# Zero init for output layer (before softmax) to zero
nn.init.zeros_(self.linear_no_bias_pae.weight)
nn.init.zeros_(self.linear_no_bias_pde.weight)
nn.init.zeros_(self.plddt_weight)
nn.init.zeros_(self.resolved_weight)
# Zero init for trunk embedding input layer
# nn.init.zeros_(self.linear_no_bias_s_trunk.weight)
# nn.init.zeros_(self.linear_no_bias_z_trunk.weight)
def forward(
self,
input_feature_dict: dict[str, Union[torch.Tensor, int, float, dict]],
s_inputs: torch.Tensor,
s_trunk: torch.Tensor,
z_trunk: torch.Tensor,
pair_mask: torch.Tensor,
x_pred_coords: 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, torch.Tensor, torch.Tensor]:
"""
Args:
input_feature_dict: Dictionary containing input features.
s_inputs (torch.Tensor): single embedding from InputFeatureEmbedder
[..., N_tokens, c_s_inputs]
s_trunk (torch.Tensor): single feature embedding from PairFormer (Alg17)
[..., N_tokens, c_s]
z_trunk (torch.Tensor): pair feature embedding from PairFormer (Alg17)
[..., N_tokens, N_tokens, c_z]
pair_mask (torch.Tensor): pair mask
[..., N_token, N_token]
x_pred_coords (torch.Tensor): predicted coordinates
[..., N_sample, N_atoms, 3]
use_memory_efficient_kernel (bool, optional): Whether to use memory-efficient kernel. Defaults to False.
use_deepspeed_evo_attention (bool, optional): Whether to use DeepSpeed evolutionary attention. Defaults to False.
use_lma (bool, optional): Whether to use low-memory attention. Defaults to False.
inplace_safe (bool, optional): Whether to use inplace operations. Defaults to False.
chunk_size (Optional[int], optional): Chunk size for memory-efficient operations. Defaults to None.
Returns:
tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
- plddt_preds: Predicted pLDDT scores [..., N_sample, N_atom, plddt_bins].
- pae_preds: Predicted PAE scores [..., N_sample, N_token, N_token, pae_bins].
- pde_preds: Predicted PDE scores [..., N_sample, N_token, N_token, pde_bins].
- resolved_preds: Predicted resolved scores [..., N_sample, N_atom, 2].
"""
if self.stop_gradient:
s_inputs = s_inputs.detach()
s_trunk = s_trunk.detach()
z_trunk = z_trunk.detach()
s_trunk = self.linear_no_bias_s_trunk(self.layernorm_s_trunk(s_trunk))
z_trunk = self.linear_no_bias_z_trunk(self.layernorm_z_trunk(z_trunk))
z_init = (
self.linear_no_bias_s1(s_inputs)[..., None, :, :]
+ self.linear_no_bias_s2(s_inputs)[..., None, :]
)
s_init = self.linear_no_bias_s_inputs(s_inputs)
s_trunk = torch.cat([s_init, s_trunk], dim=-1)
z_trunk = torch.cat([z_init, z_trunk], dim=-1)
s_trunk = self.linear_no_bias_s_cat(self.layernorm_no_bias_s_cat(s_trunk))
z_trunk = self.linear_no_bias_z_cat(self.layernorm_no_bias_z_cat(z_trunk))
if not self.training:
del z_init
torch.cuda.empty_cache()
x_rep_atom_mask = input_feature_dict[
"distogram_rep_atom_mask"
].bool() # [N_atom]
x_pred_rep_coords = x_pred_coords[..., x_rep_atom_mask, :]
N_sample = x_pred_rep_coords.size(-3)
plddt_preds, pae_preds, pde_preds, resolved_preds = [], [], [], []
for i in range(N_sample):
plddt_pred, pae_pred, pde_pred, resolved_pred = (
self.memory_efficient_forward(
input_feature_dict=input_feature_dict,
s_trunk=s_trunk.clone() if inplace_safe else s_trunk,
z_pair=z_trunk.clone() if inplace_safe else z_trunk,
pair_mask=pair_mask,
x_pred_rep_coords=x_pred_rep_coords[..., i, :, :],
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 z_trunk.shape[-2] > 2000 and (not self.training):
# cpu offload pae_preds/pde_preds
pae_pred = pae_pred.cpu()
pde_pred = pde_pred.cpu()
torch.cuda.empty_cache()
plddt_preds.append(plddt_pred)
pae_preds.append(pae_pred)
pde_preds.append(pde_pred)
resolved_preds.append(resolved_pred)
plddt_preds = torch.stack(
plddt_preds, dim=-3
) # [..., N_sample, N_atom, plddt_bins]
# Pae_preds/pde_preds single tensor will occupy 11.6G[BF16]/23.2G[FP32]
pae_preds = torch.stack(
pae_preds, dim=-4
) # [..., N_sample, N_token, N_token, pae_bins]
pde_preds = torch.stack(
pde_preds, dim=-4
) # [..., N_sample, N_token, N_token, pde_bins]
resolved_preds = torch.stack(
resolved_preds, dim=-3
) # [..., N_sample, N_atom, 2]
return plddt_preds, pae_preds, pde_preds, resolved_preds
def memory_efficient_forward(
self,
input_feature_dict: dict[str, Union[torch.Tensor, int, float, dict]],
s_trunk: torch.Tensor,
z_pair: torch.Tensor,
pair_mask: torch.Tensor,
x_pred_rep_coords: 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, torch.Tensor, torch.Tensor]:
"""
Args:
...
x_pred_coords (torch.Tensor): predicted coordinates
[..., N_atoms, 3] # Note: N_sample = 1 for avoiding CUDA OOM
"""
# Embed pair distances of representative atoms:
distance_pred = cdist(
x_pred_rep_coords, x_pred_rep_coords
) # [..., N_tokens, N_tokens]
if inplace_safe:
z_pair += self.linear_no_bias_d(
one_hot(
x=distance_pred,
lower_bins=self.lower_bins,
upper_bins=self.upper_bins,
)
) # [..., N_tokens, N_tokens, c_z]
else:
z_pair = z_pair + self.linear_no_bias_d(
one_hot(
x=distance_pred,
lower_bins=self.lower_bins,
upper_bins=self.upper_bins,
)
) # [..., N_tokens, N_tokens, c_z]
# Line 4
s_single, z_pair = self.pairformer_stack(
s_trunk,
z_pair,
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,
)
pae_pred = self.linear_no_bias_pae(self.pae_ln(z_pair))
pde_pred = self.linear_no_bias_pde(
self.pde_ln(z_pair + z_pair.transpose(-2, -3))
)
atom_to_token_idx = input_feature_dict[
"atom_to_token_idx"
] # in range [0, N_token-1] shape: [N_atom]
atom_to_tokatom_idx = input_feature_dict[
"atom_to_tokatom_idx"
] # in range [0, max_atoms_per_token-1] shape: [N_atom] # influenced by crop
# Broadcast s_single: [N_tokens, c_s] -> [N_atoms, c_s]
a = broadcast_token_to_atom(
x_token=s_single, atom_to_token_idx=atom_to_token_idx
)
plddt_pred = torch.einsum(
"...nc,ncb->...nb", self.plddt_ln(a), self.plddt_weight[atom_to_tokatom_idx]
)
resolved_pred = torch.einsum(
"...nc,ncb->...nb",
self.resolved_ln(a),
self.resolved_weight[atom_to_tokatom_idx],
)
if not self.training and z_pair.shape[-2] > 2000:
torch.cuda.empty_cache()
return plddt_pred, pae_pred, pde_pred, resolved_pred
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