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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from ..modules import (
AxialTransformerLayer,
LearnedPositionalEmbedding,
RobertaLMHead,
ESM1bLayerNorm,
ContactPredictionHead,
)
from ..axial_attention import RowSelfAttention, ColumnSelfAttention
class MSATransformer(nn.Module):
@classmethod
def add_args(cls, parser):
# fmt: off
parser.add_argument(
"--num_layers",
default=12,
type=int,
metavar="N",
help="number of layers"
)
parser.add_argument(
"--embed_dim",
default=768,
type=int,
metavar="N",
help="embedding dimension"
)
parser.add_argument(
"--logit_bias",
action="store_true",
help="whether to apply bias to logits"
)
parser.add_argument(
"--ffn_embed_dim",
default=3072,
type=int,
metavar="N",
help="embedding dimension for FFN",
)
parser.add_argument(
"--attention_heads",
default=12,
type=int,
metavar="N",
help="number of attention heads",
)
parser.add_argument(
"--dropout",
default=0.1,
type=float,
help="Dropout to apply."
)
parser.add_argument(
"--attention_dropout",
default=0.1,
type=float,
help="Dropout to apply."
)
parser.add_argument(
"--activation_dropout",
default=0.1,
type=float,
help="Dropout to apply."
)
parser.add_argument(
"--max_tokens_per_msa",
default=2 ** 14,
type=int,
help=(
"Used during inference to batch attention computations in a single "
"forward pass. This allows increased input sizes with less memory."
),
)
# fmt: on
def __init__(self, args, alphabet):
super().__init__()
self.args = args
self.alphabet_size = len(alphabet)
self.padding_idx = alphabet.padding_idx
self.mask_idx = alphabet.mask_idx
self.cls_idx = alphabet.cls_idx
self.eos_idx = alphabet.eos_idx
self.prepend_bos = alphabet.prepend_bos
self.append_eos = alphabet.append_eos
self.embed_tokens = nn.Embedding(
self.alphabet_size, self.args.embed_dim, padding_idx=self.padding_idx
)
if getattr(self.args, "embed_positions_msa", False):
emb_dim = getattr(self.args, "embed_positions_msa_dim", self.args.embed_dim)
self.msa_position_embedding = nn.Parameter(
0.01 * torch.randn(1, 1024, 1, emb_dim),
requires_grad=True,
)
else:
self.register_parameter("msa_position_embedding", None)
self.dropout_module = nn.Dropout(self.args.dropout)
self.layers = nn.ModuleList(
[
AxialTransformerLayer(
self.args.embed_dim,
self.args.ffn_embed_dim,
self.args.attention_heads,
self.args.dropout,
self.args.attention_dropout,
self.args.activation_dropout,
getattr(self.args, "max_tokens_per_msa", self.args.max_tokens),
)
for _ in range(self.args.layers)
]
)
self.contact_head = ContactPredictionHead(
self.args.layers * self.args.attention_heads,
self.prepend_bos,
self.append_eos,
eos_idx=self.eos_idx,
)
self.embed_positions = LearnedPositionalEmbedding(
self.args.max_positions,
self.args.embed_dim,
self.padding_idx,
)
self.emb_layer_norm_before = ESM1bLayerNorm(self.args.embed_dim)
self.emb_layer_norm_after = ESM1bLayerNorm(self.args.embed_dim)
self.lm_head = RobertaLMHead(
embed_dim=self.args.embed_dim,
output_dim=self.alphabet_size,
weight=self.embed_tokens.weight,
)
def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False):
if return_contacts:
need_head_weights = True
assert tokens.ndim == 3
batch_size, num_alignments, seqlen = tokens.size()
padding_mask = tokens.eq(self.padding_idx) # B, R, C
if not padding_mask.any():
padding_mask = None
x = self.embed_tokens(tokens)
x += self.embed_positions(tokens.view(batch_size * num_alignments, seqlen)).view(x.size())
if self.msa_position_embedding is not None:
if x.size(1) > 1024:
raise RuntimeError(
"Using model with MSA position embedding trained on maximum MSA "
f"depth of 1024, but received {x.size(1)} alignments."
)
x += self.msa_position_embedding[:, :num_alignments]
x = self.emb_layer_norm_before(x)
x = self.dropout_module(x)
if padding_mask is not None:
x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))
repr_layers = set(repr_layers)
hidden_representations = {}
if 0 in repr_layers:
hidden_representations[0] = x
if need_head_weights:
row_attn_weights = []
col_attn_weights = []
# B x R x C x D -> R x C x B x D
x = x.permute(1, 2, 0, 3)
for layer_idx, layer in enumerate(self.layers):
x = layer(
x,
self_attn_padding_mask=padding_mask,
need_head_weights=need_head_weights,
)
if need_head_weights:
x, col_attn, row_attn = x
# H x C x B x R x R -> B x H x C x R x R
col_attn_weights.append(col_attn.permute(2, 0, 1, 3, 4))
# H x B x C x C -> B x H x C x C
row_attn_weights.append(row_attn.permute(1, 0, 2, 3))
if (layer_idx + 1) in repr_layers:
hidden_representations[layer_idx + 1] = x.permute(2, 0, 1, 3)
x = self.emb_layer_norm_after(x)
x = x.permute(2, 0, 1, 3) # R x C x B x D -> B x R x C x D
# last hidden representation should have layer norm applied
if (layer_idx + 1) in repr_layers:
hidden_representations[layer_idx + 1] = x
x = self.lm_head(x)
result = {"logits": x, "representations": hidden_representations}
if need_head_weights:
# col_attentions: B x L x H x C x R x R
col_attentions = torch.stack(col_attn_weights, 1)
# row_attentions: B x L x H x C x C
row_attentions = torch.stack(row_attn_weights, 1)
result["col_attentions"] = col_attentions
result["row_attentions"] = row_attentions
if return_contacts:
contacts = self.contact_head(tokens, row_attentions)
result["contacts"] = contacts
return result
def predict_contacts(self, tokens):
return self(tokens, return_contacts=True)["contacts"]
@property
def num_layers(self):
return self.args.layers
def max_tokens_per_msa_(self, value: int) -> None:
"""The MSA Transformer automatically batches attention computations when
gradients are disabled to allow you to pass in larger MSAs at test time than
you can fit in GPU memory. By default this occurs when more than 2^14 tokens
are passed in the input MSA. You can set this value to infinity to disable
this behavior.
"""
for module in self.modules():
if isinstance(module, (RowSelfAttention, ColumnSelfAttention)):
module.max_tokens_per_msa = value
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