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"""Per-atom cross attention."""
import dataclasses
from flax_model.alphafold3.common import base_config
from flax_model.alphafold3.model import feat_batch
from flax_model.alphafold3.model import model_config
from flax_model.alphafold3.model.atom_layout import atom_layout
from flax_model.alphafold3.model.components import haiku_modules as hm
from flax_model.alphafold3.model.components import utils
from flax_model.alphafold3.model.network import diffusion_transformer
# import chex
import jax
import jax.numpy as jnp
class AtomCrossAttEncoderConfig(base_config.BaseConfig):
per_token_channels: int = 768
per_atom_channels: int = 128
atom_transformer: diffusion_transformer.CrossAttTransformer.Config = (
base_config.autocreate(num_intermediate_factor=2, num_blocks=3)
)
per_atom_pair_channels: int = 16
def _per_atom_conditioning(
config: AtomCrossAttEncoderConfig, batch: feat_batch.Batch, name: str
) -> tuple[jnp.ndarray, jnp.ndarray]:
"""computes single and pair conditioning for all atoms in each token."""
c = config
# Compute per-atom single conditioning
# Shape (num_tokens, num_dense, channels)
act = hm.Linear(
c.per_atom_channels, precision='highest', name=f'{name}_embed_ref_pos'
)(batch.ref_structure.positions)
act += hm.Linear(c.per_atom_channels, name=f'{name}_embed_ref_mask')(
batch.ref_structure.mask.astype(jnp.float32)[:, :, None]
)
# Element is encoded as atomic number if the periodic table, so
# 128 should be fine.
act += hm.Linear(c.per_atom_channels, name=f'{name}_embed_ref_element')(
jax.nn.one_hot(batch.ref_structure.element, 128)
)
act += hm.Linear(c.per_atom_channels, name=f'{name}_embed_ref_charge')(
jnp.arcsinh(batch.ref_structure.charge)[:, :, None]
)
# Characters are encoded as ASCII code minus 32, so we need 64 classes,
# to encode all standard ASCII characters between 32 and 96.
atom_name_chars_1hot = jax.nn.one_hot(batch.ref_structure.atom_name_chars, 64)
num_token, num_dense, _ = act.shape
act += hm.Linear(c.per_atom_channels, name=f'{name}_embed_ref_atom_name')(
atom_name_chars_1hot.reshape(num_token, num_dense, -1)
)
act *= batch.ref_structure.mask.astype(jnp.float32)[:, :, None]
# Compute pair conditioning
# shape (num_tokens, num_dense, num_dense, channels)
# Embed single features
row_act = hm.Linear(
c.per_atom_pair_channels, name=f'{name}_single_to_pair_cond_row'
)(jax.nn.relu(act))
col_act = hm.Linear(
c.per_atom_pair_channels, name=f'{name}_single_to_pair_cond_col'
)(jax.nn.relu(act))
pair_act = row_act[:, :, None, :] + col_act[:, None, :, :]
# Embed pairwise offsets
pair_act += hm.Linear(
c.per_atom_pair_channels,
precision='highest',
name=f'{name}_embed_pair_offsets',
)(
batch.ref_structure.positions[:, :, None, :]
- batch.ref_structure.positions[:, None, :, :]
)
# Embed pairwise inverse squared distances
sq_dists = jnp.sum(
jnp.square(
batch.ref_structure.positions[:, :, None, :]
- batch.ref_structure.positions[:, None, :, :]
),
axis=-1,
)
pair_act += hm.Linear(
c.per_atom_pair_channels, name=f'{name}_embed_pair_distances'
)(1.0 / (1 + sq_dists[:, :, :, None]))
return act, pair_act
@dataclasses.dataclass(frozen=True)
class AtomCrossAttEncoderOutput:
token_act: jnp.ndarray # (num_tokens, ch)
skip_connection: jnp.ndarray # (num_subsets, num_queries, ch)
queries_mask: jnp.ndarray # (num_subsets, num_queries)
queries_single_cond: jnp.ndarray # (num_subsets, num_queries, ch)
keys_mask: jnp.ndarray # (num_subsets, num_keys)
keys_single_cond: jnp.ndarray # (num_subsets, num_keys, ch)
pair_cond: jnp.ndarray # (num_subsets, num_queries, num_keys, ch)
jax.tree_util.register_dataclass(
AtomCrossAttEncoderOutput,
data_fields=[f.name for f in dataclasses.fields(AtomCrossAttEncoderOutput)],
meta_fields=[],
)
def atom_cross_att_encoder(
token_atoms_act: jnp.ndarray | None, # (num_tokens, max_atoms_per_token, 3)
trunk_single_cond: jnp.ndarray | None, # (num_tokens, ch)
trunk_pair_cond: jnp.ndarray | None, # (num_tokens, num_tokens, ch)
config: AtomCrossAttEncoderConfig,
global_config: model_config.GlobalConfig,
batch: feat_batch.Batch,
name: str,
) -> AtomCrossAttEncoderOutput:
"""Cross-attention on flat atom subsets and mapping to per-token features."""
c = config
# Compute single conditioning from atom meta data and convert to queries
# layout.
# (num_subsets, num_queries, channels)
token_atoms_single_cond, _ = _per_atom_conditioning(config, batch, name)
token_atoms_mask = batch.predicted_structure_info.atom_mask
queries_single_cond = atom_layout.convert(
batch.atom_cross_att.token_atoms_to_queries,
token_atoms_single_cond,
layout_axes=(-3, -2),
)
queries_mask = atom_layout.convert(
batch.atom_cross_att.token_atoms_to_queries,
token_atoms_mask,
layout_axes=(-2, -1),
)
# If provided, broadcast single conditioning from trunk to all queries
if trunk_single_cond is not None:
trunk_single_cond = hm.Linear(
c.per_atom_channels,
precision='highest',
initializer=global_config.final_init,
name=f'{name}_embed_trunk_single_cond',
)(
hm.LayerNorm(
use_fast_variance=False,
create_offset=False,
name=f'{name}_lnorm_trunk_single_cond',
)(trunk_single_cond)
)
queries_single_cond += atom_layout.convert(
batch.atom_cross_att.tokens_to_queries,
trunk_single_cond,
layout_axes=(-2,),
)
if token_atoms_act is None:
# if no token_atoms_act is given (e.g. begin of evoformer), we use the
# static conditioning only
queries_act = queries_single_cond
else:
# Convert token_atoms_act to queries layout and map to per_atom_channels
# (num_subsets, num_queries, channels)
queries_act = atom_layout.convert(
batch.atom_cross_att.token_atoms_to_queries,
token_atoms_act,
layout_axes=(-3, -2),
)
queries_act = hm.Linear(
c.per_atom_channels,
precision='highest',
name=f'{name}_atom_positions_to_features',
)(queries_act)
queries_act *= queries_mask[..., None]
queries_act += queries_single_cond
# Gather the keys from the queries.
keys_single_cond = atom_layout.convert(
batch.atom_cross_att.queries_to_keys,
queries_single_cond,
layout_axes=(-3, -2),
)
keys_mask = atom_layout.convert(
batch.atom_cross_att.queries_to_keys, queries_mask, layout_axes=(-2, -1)
)
# Embed single features into the pair conditioning.
# shape (num_subsets, num_queries, num_keys, ch)
row_act = hm.Linear(
c.per_atom_pair_channels, name=f'{name}_single_to_pair_cond_row'
)(jax.nn.relu(queries_single_cond))
pair_cond_keys_input = atom_layout.convert(
batch.atom_cross_att.queries_to_keys,
queries_single_cond,
layout_axes=(-3, -2),
)
col_act = hm.Linear(
c.per_atom_pair_channels, name=f'{name}_single_to_pair_cond_col'
)(jax.nn.relu(pair_cond_keys_input))
pair_act = row_act[:, :, None, :] + col_act[:, None, :, :]
if trunk_pair_cond is not None:
# If provided, broadcast the pair conditioning for the trunk (evoformer
# pairs) to the atom pair activations. This should boost ligands, but also
# help for cross attention within proteins, because we always have atoms
# from multiple residues in a subset.
# Map trunk pair conditioning to per_atom_pair_channels
# (num_tokens, num_tokens, per_atom_pair_channels)
trunk_pair_cond = hm.Linear(
c.per_atom_pair_channels,
precision='highest',
initializer=global_config.final_init,
name=f'{name}_embed_trunk_pair_cond',
)(
hm.LayerNorm(
use_fast_variance=False,
create_offset=False,
name=f'{name}_lnorm_trunk_pair_cond',
)(trunk_pair_cond)
)
# Create the GatherInfo into a flattened trunk_pair_cond from the
# queries and keys gather infos.
num_tokens = trunk_pair_cond.shape[0]
# (num_subsets, num_queries)
tokens_to_queries = batch.atom_cross_att.tokens_to_queries
# (num_subsets, num_keys)
tokens_to_keys = batch.atom_cross_att.tokens_to_keys
# (num_subsets, num_queries, num_keys)
trunk_pair_to_atom_pair = atom_layout.GatherInfo(
gather_idxs=(
num_tokens * tokens_to_queries.gather_idxs[:, :, None]
+ tokens_to_keys.gather_idxs[:, None, :]
),
gather_mask=(
tokens_to_queries.gather_mask[:, :, None]
& tokens_to_keys.gather_mask[:, None, :]
),
input_shape=jnp.array((num_tokens, num_tokens)),
)
# Gather the conditioning and add it to the atom-pair activations.
pair_act += atom_layout.convert(
trunk_pair_to_atom_pair, trunk_pair_cond, layout_axes=(-3, -2)
)
# Embed pairwise offsets
queries_ref_pos = atom_layout.convert(
batch.atom_cross_att.token_atoms_to_queries,
batch.ref_structure.positions,
layout_axes=(-3, -2),
)
queries_ref_space_uid = atom_layout.convert(
batch.atom_cross_att.token_atoms_to_queries,
batch.ref_structure.ref_space_uid,
layout_axes=(-2, -1),
)
keys_ref_pos = atom_layout.convert(
batch.atom_cross_att.queries_to_keys,
queries_ref_pos,
layout_axes=(-3, -2),
)
keys_ref_space_uid = atom_layout.convert(
batch.atom_cross_att.queries_to_keys,
batch.ref_structure.ref_space_uid,
layout_axes=(-2, -1),
)
offsets_valid = (
queries_ref_space_uid[:, :, None] == keys_ref_space_uid[:, None, :]
)
offsets = queries_ref_pos[:, :, None, :] - keys_ref_pos[:, None, :, :]
pair_act += (
hm.Linear(
c.per_atom_pair_channels,
precision='highest',
name=f'{name}_embed_pair_offsets',
)(offsets)
* offsets_valid[:, :, :, None]
)
# Embed pairwise inverse squared distances
sq_dists = jnp.sum(jnp.square(offsets), axis=-1)
pair_act += (
hm.Linear(c.per_atom_pair_channels, name=f'{name}_embed_pair_distances')(
1.0 / (1 + sq_dists[:, :, :, None])
)
* offsets_valid[:, :, :, None]
)
# Embed offsets valid mask
pair_act += hm.Linear(
c.per_atom_pair_channels, name=f'{name}_embed_pair_offsets_valid'
)(offsets_valid[:, :, :, None].astype(jnp.float32))
# Run a small MLP on the pair acitvations
pair_act2 = hm.Linear(
c.per_atom_pair_channels, initializer='relu', name=f'{name}_pair_mlp_1'
)(jax.nn.relu(pair_act))
pair_act2 = hm.Linear(
c.per_atom_pair_channels, initializer='relu', name=f'{name}_pair_mlp_2'
)(jax.nn.relu(pair_act2))
pair_act += hm.Linear(
c.per_atom_pair_channels,
initializer=global_config.final_init,
name=f'{name}_pair_mlp_3',
)(jax.nn.relu(pair_act2))
# Run the atom cross attention transformer.
queries_act = diffusion_transformer.CrossAttTransformer(
c.atom_transformer, global_config, name=f'{name}_atom_transformer_encoder'
)(
queries_act=queries_act,
queries_mask=queries_mask,
queries_to_keys=batch.atom_cross_att.queries_to_keys,
keys_mask=keys_mask,
queries_single_cond=queries_single_cond,
keys_single_cond=keys_single_cond,
pair_cond=pair_act,
)
queries_act *= queries_mask[..., None]
skip_connection = queries_act
# Convert back to token-atom layout and aggregate to tokens
queries_act = hm.Linear(
c.per_token_channels, name=f'{name}_project_atom_features_for_aggr'
)(queries_act)
token_atoms_act = atom_layout.convert(
batch.atom_cross_att.queries_to_token_atoms,
queries_act,
layout_axes=(-3, -2),
)
token_act = utils.mask_mean(
token_atoms_mask[..., None], jax.nn.relu(token_atoms_act), axis=-2
)
return AtomCrossAttEncoderOutput(
token_act=token_act,
skip_connection=skip_connection,
queries_mask=queries_mask,
queries_single_cond=queries_single_cond,
keys_mask=keys_mask,
keys_single_cond=keys_single_cond,
pair_cond=pair_act,
)
class AtomCrossAttDecoderConfig(base_config.BaseConfig):
per_atom_channels: int = 128
atom_transformer: diffusion_transformer.CrossAttTransformer.Config = (
base_config.autocreate(num_intermediate_factor=2, num_blocks=3)
)
def atom_cross_att_decoder(
token_act: jnp.ndarray, # (num_tokens, ch)
enc: AtomCrossAttEncoderOutput,
config: AtomCrossAttDecoderConfig,
global_config: model_config.GlobalConfig,
batch: feat_batch.Batch,
name: str,
): # (num_tokens, max_atoms_per_token, 3)
"""Mapping to per-atom features and self-attention on subsets."""
c = config
# map per-token act down to per_atom channels
token_act = hm.Linear(
c.per_atom_channels, name=f'{name}_project_token_features_for_broadcast'
)(token_act)
# Broadcast to token-atoms layout and convert to queries layout.
num_token, max_atoms_per_token = (
batch.atom_cross_att.queries_to_token_atoms.shape
)
token_atom_act = jnp.broadcast_to(
token_act[:, None, :],
(num_token, max_atoms_per_token, c.per_atom_channels),
)
queries_act = atom_layout.convert(
batch.atom_cross_att.token_atoms_to_queries,
token_atom_act,
layout_axes=(-3, -2),
)
queries_act += enc.skip_connection
queries_act *= enc.queries_mask[..., None]
# Run the atom cross attention transformer.
queries_act = diffusion_transformer.CrossAttTransformer(
c.atom_transformer, global_config, name=f'{name}_atom_transformer_decoder'
)(
queries_act=queries_act,
queries_mask=enc.queries_mask,
queries_to_keys=batch.atom_cross_att.queries_to_keys,
keys_mask=enc.keys_mask,
queries_single_cond=enc.queries_single_cond,
keys_single_cond=enc.keys_single_cond,
pair_cond=enc.pair_cond,
)
queries_act *= enc.queries_mask[..., None]
queries_act = hm.LayerNorm(
use_fast_variance=False,
create_offset=False,
name=f'{name}_atom_features_layer_norm',
)(queries_act)
queries_position_update = hm.Linear(
3,
initializer=global_config.final_init,
precision='highest',
name=f'{name}_atom_features_to_position_update',
)(queries_act)
position_update = atom_layout.convert(
batch.atom_cross_att.queries_to_token_atoms,
queries_position_update,
layout_axes=(-3, -2),
)
return position_update
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