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#
# 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.
import math
from functools import partial
from typing import Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.utils.model.misc import (
flatten_final_dims,
move_final_dim_to_dim,
pad_at_dim,
reshape_at_dim,
)
from src.utils.openfold_local.model.primitives import LayerNorm, trunc_normal_init_
from src.utils.openfold_local.utils.chunk_utils import chunk_layer
class Linear(nn.Linear):
"""
Linear layer with customizable initialization and optional precision control.
This module extends torch.nn.Linear with support for:
- Different initialization schemes (default, relu, zeros)
- Optional high-precision computation
- Customizable bias initialization
"""
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
device: torch.device = None,
dtype: torch.dtype = None,
precision: torch.dtype = None,
initializer: str = "default",
) -> None:
"""
Initialize the Linear layer.
Args:
in_features (int): Size of each input sample.
out_features (int): Size of each output sample.
bias (bool, optional): If True, adds a learnable bias to the output.
Defaults to True.
device (torch.device, optional): Device on which to create the parameters.
Defaults to None.
dtype (torch.dtype, optional): Data type of the parameters.
Defaults to None.
precision (torch.dtype, optional): If specified, forces computation
in this precision regardless of input dtype. Useful for numerical
stability. Defaults to None.
initializer (str, optional): Weight initialization method.
Options: 'default' (scale=1.0), 'relu' (scale=2.0), 'zeros'.
Defaults to "default".
"""
self.use_bias = bias
self.precision = precision
self.initializer = initializer
super().__init__(
in_features=in_features,
out_features=out_features,
bias=bias,
device=device,
dtype=dtype,
)
self._init_params()
@torch.no_grad()
def _init_params(self) -> None:
"""
Initialize layer parameters according to the specified initialization scheme.
Bias is always initialized to zero. Weight initialization depends on
the initializer argument:
- 'default': Truncated normal with scale 1.0
- 'relu': Truncated normal with scale 2.0 (suitable for ReLU activations)
- 'zeros': All zeros
"""
if self.use_bias:
nn.init.zeros_(self.bias) # zero-init bias
if self.initializer == "default":
trunc_normal_init_(self.weight, scale=1.0)
elif self.initializer == "relu":
trunc_normal_init_(self.weight, scale=2.0)
elif self.initializer == "zeros":
nn.init.zeros_(self.weight)
else:
raise ValueError(f"Invalid initializer: {self.initializer}.")
def forward(self, input: torch.Tensor) -> torch.Tensor:
if self.precision is not None:
input_dtype = input.dtype
with torch.cuda.amp.autocast(enabled=False):
bias = (
self.bias.to(dtype=self.precision)
if self.bias is not None
else None
)
return F.linear(
input.to(dtype=self.precision),
self.weight.to(dtype=self.precision),
bias,
).to(dtype=input_dtype)
else:
return F.linear(input, self.weight, self.bias)
LinearNoBias = partial(Linear, bias=False)
class AdaptiveLayerNorm(nn.Module):
"""
Implements Algorithm 26 in AF3
"""
def __init__(self, c_a: int = 768, c_s: int = 384) -> None:
"""
Args:
c_a (int, optional): the embedding dim of a(single feature aggregated atom info). Defaults to 768.
c_s (int, optional): hidden dim [for single embedding]. Defaults to 384.
"""
super(AdaptiveLayerNorm, self).__init__()
self.layernorm_a = LayerNorm(c_a, create_scale=False, create_offset=False)
self.layernorm_s = LayerNorm(c_s, create_offset=False)
self.linear_s = Linear(in_features=c_s, out_features=c_a, initializer="zeros")
self.linear_nobias_s = LinearNoBias(
in_features=c_s, out_features=c_a, initializer="zeros"
)
def forward(self, a: torch.Tensor, s: torch.Tensor) -> torch.Tensor:
"""
Args:
a (torch.Tensor): the single feature aggregate per-atom representation
[..., N_token, c_a]
s (torch.Tensor): single embedding
[..., N_token, c_s]
Returns:
torch.Tensor: the updated a from AdaLN
[..., N_token, c_a]
"""
a = self.layernorm_a(a)
s = self.layernorm_s(s)
a = torch.sigmoid(self.linear_s(s)) * a + self.linear_nobias_s(s)
return a
class BiasInitLinear(Linear):
"""Support biasinit for nn.Linear Called just like torch.nn.Linear."""
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
biasinit: float = 0.0,
**kwargs,
) -> None:
"""
Args:
in_features (int): in_features
out_features (int): out_features
bias (bool, optional): whether add bias. Defaults to True.
biasinit (float, optional): the initial bias value. Defaults to 0.0.
"""
super(BiasInitLinear, self).__init__(
in_features=in_features, out_features=out_features, bias=bias, **kwargs
)
nn.init.zeros_(tensor=self.weight)
if bias:
nn.init.constant_(tensor=self.bias, val=biasinit)
class Transition(nn.Module):
"""
Implements Algorithm 11 in AF3
"""
def __init__(self, c_in: int, n: int) -> None:
"""
Args:
c_in (int, optional): the input dimension.
n (int, optional): factor by which c_in is multiplied to obtain hidden dimension.
"""
super(Transition, self).__init__()
self.n = n
self.c_in = c_in
self.layernorm1 = LayerNorm(c_in)
self.linear_no_bias_a = LinearNoBias(
in_features=c_in, out_features=n * c_in, initializer="relu"
)
self.linear_no_bias_b = LinearNoBias(
in_features=c_in, out_features=n * c_in, initializer="relu"
)
self.linear_no_bias = LinearNoBias(
in_features=n * c_in, out_features=c_in, initializer="zeros"
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x (torch.Tensor): the input tensor
[..., c]
Returns:
torch.Tensor: the output tensor as the same shape of x
[..., c]
"""
if self.training:
x = self.layernorm1(x)
a = self.linear_no_bias_a(x)
b = self.linear_no_bias_b(x)
x = self.linear_no_bias(F.silu(a) * b)
return x
else:
other_dims = x.shape[:-1]
dim_size = x.shape[-1]
size = x.shape[-2]
x = x.reshape(-1, dim_size)
chunk_num = 1 if size < 3200 else 8
chunks = torch.chunk(x, chunk_num, dim=-2)
outputs = torch.empty(
(x.shape[0], self.c_in), dtype=x.dtype, device=x.device
)
start = 0
for chunk in chunks:
y = self.layernorm1(chunk)
a = self.linear_no_bias_a(y)
a = F.silu(a, True)
b = self.linear_no_bias_b(y)
del y
b *= a
del a
b = self.linear_no_bias(b)
outputs[start : start + b.shape[0]] = b
start += b.shape[0]
del b
outputs = outputs.reshape(*other_dims, self.c_in)
return outputs
def _attention(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
attn_bias: Optional[torch.Tensor] = None,
use_efficient_implementation: bool = False,
inplace_safe: bool = False,
) -> torch.Tensor:
"""Attention.
Args:
q (torch.Tensor): query tensor of shape [..., n_q, d]
k (torch.Tensor): key tensor of shape [..., n_kv, d]
v (torch.Tensor): value tensor of shape[..., n_kv, d]
attn_bias (torch.Tensor, optional): attention bias tensor of shape [..., n_q, n_kv]. Defaults to None.
use_efficient_implementation (bool): whether to use the torch.nn.functional.scaled_dot_product_attention, Defaults to False.
Returns:
torch.Tensor: output of tensor [..., n_q, d]
"""
assert k.shape == v.shape
# Upcast to compute attn_weights
input_dtype = q.dtype
q = q.to(dtype=torch.float32)
k = k.to(dtype=torch.float32)
if attn_bias is not None:
attn_bias = attn_bias.to(dtype=torch.float32)
if use_efficient_implementation:
attn_output = F.scaled_dot_product_attention(
query=q,
key=k,
value=v,
attn_mask=attn_bias,
)
return attn_output
with torch.cuda.amp.autocast(enabled=False):
# [..., n_kv, d] -> [..., d, n_kv]
k = k.transpose(-1, -2)
# [..., n_q, d], [..., d, n_kv] -> [..., n_q, n_kv]
attn_weights = q @ k
if attn_bias is not None:
if inplace_safe:
attn_weights += attn_bias
else:
attn_weights = attn_weights + attn_bias
# [..., n_q, n_kv]
attn_weights = F.softmax(attn_weights, dim=-1)
# [..., n_q, n_kv], [..., n_kv, d] -> [..., n_q, d]
attn_output = attn_weights.to(dtype=input_dtype) @ v
return attn_output
def rearrange_qk_to_dense_trunk(
q: Union[torch.Tensor, list[torch.Tensor]],
k: Union[torch.Tensor, list[torch.Tensor]],
dim_q: Union[int, list[int]],
dim_k: Union[int, list[int]],
n_queries: int = 32,
n_keys: int = 128,
compute_mask: bool = True,
) -> tuple[Union[torch.Tensor, list[torch.Tensor]]]:
"""Rearrange q/k into blocked tensors for local operations.
Args:
q (torch.Tensor): query tensor. Could be a tensor or a list of tensors.
[..., n_q, ...] (n_q is at dimension dim_q)
k (torch.Tensor | List[torch.Tensor]): key tensor. Could be a tensor or a list of tensors.
[..., n_k, ...] (n_k is at dimension dim_k)
dim_q (int): along which dimension to build the trunks. Could be an int or a list of int.
dim_k (int): along which dimension to build the trunks. Could be an int or a list of int.
n_queries (int, optional): local window size of query tensor.
n_keys (int, optional): local window size of key/value tensor.
Returns:
tuple[Union[torch.Tensor, list[torch.Tensor]]]:
q_trunked: torch.Tensor or list of tensors. Same as the input type.
[..., n_trunks, n_queries, ...]
k_trunked: torch.Tensor or list of tensors. Same as the input type.
[..., n_trunks, n_keys, ...]
padding_info (dict):
mask_trunked: torch.Tensor
[n_trunks, n_queries, n_keys]
q_pad: query padded dimension
"""
assert n_keys >= n_queries
assert n_queries & 0x01 == 0
assert n_keys & 0x01 == 0
def basic_checks(x, dim_x):
if isinstance(x, list):
x_is_list = True
assert isinstance(dim_x, list)
else:
x_is_list = False
x = [x]
dim_x = [dim_x]
n_x = x[0].size(dim_x[0])
for i in range(len(dim_x)):
if dim_x[i] < 0:
dim_x[i] = len(x[i].shape) + dim_x[i]
assert x[i].size(dim_x[i]) == n_x
return x, dim_x, x_is_list, n_x, len(x)
q, dim_q, q_is_list, n, num_q = basic_checks(q, dim_q)
k, dim_k, k_is_list, n_k, num_k = basic_checks(k, dim_k)
assert n == n_k
n_trunks = int(math.ceil(n / n_queries))
q_pad_length = n_trunks * n_queries - n
q_new = [
pad_at_dim(q[i], dim=dim_q[i], pad_length=(0, q_pad_length))
for i in range(num_q)
]
q_trunked = [
reshape_at_dim(q_new[i], dim=dim_q[i], target_shape=(n_trunks, n_queries))
for i in range(num_q)
]
pad_left = (n_keys - n_queries) // 2
pad_right = int((n_trunks - 1 / 2) * n_queries + n_keys / 2 - n + 1 / 2)
k_new = [
pad_at_dim(k[i], dim=dim_k[i], pad_length=(pad_left, pad_right))
for i in range(num_k)
]
k_trunked = [
k_new[i].unfold(dim_k[i], size=n_keys, step=n_queries) for i in range(num_k)
]
k_trunked = [
move_final_dim_to_dim(k_trunked[i], dim=dim_k[i] + 1) for i in range(num_k)
]
if compute_mask:
pad_mask = q[0].new_ones(
*(1,) * len(q[0].shape[:-2]),
n + q_pad_length,
n + pad_left + pad_right,
requires_grad=False,
)
pad_mask[..., :n, 0:pad_left] = 0
pad_mask[..., :n, pad_left + n : :] = 0
pad_mask[..., n::, :] = 0
concat_split_data = optimized_concat_split(pad_mask, n_queries)
pad_mask_trunked = (
concat_split_data.unfold(
-1, n_keys, pad_mask.size(-1) + n_queries
).transpose(-2, -3)
).bool()
else:
pad_mask_trunked = None
if not q_is_list:
q_trunked = q_trunked[0]
if not k_is_list:
k_trunked = k_trunked[0]
padding_info = {
"mask_trunked": pad_mask_trunked,
"q_pad": q_pad_length,
"k_pad_left": pad_left,
"k_pad_right": pad_right,
}
return q_trunked, k_trunked, padding_info
def optimized_concat_split(attn_bias: torch.Tensor, n_queries: int) -> torch.Tensor:
"""Optimized concatenation and splitting of attention bias tensor.
Args:
attn_bias (torch.Tensor): The attention bias tensor.
Shape: [..., D, E]
n_queries (int): The number of queries in each split.
Returns:
torch.Tensor: The reshaped and permuted attention bias tensor.
Shape: [..., n_queries, D // n_queries * E]
"""
D = attn_bias.size(-2)
E = attn_bias.size(-1)
assert D % n_queries == 0
num_splits = D // n_queries
reshaped = attn_bias.reshape(*attn_bias.shape[:-2], num_splits, n_queries, E)
permuted = reshaped.permute(*range(reshaped.dim() - 3), -2, -3, -1)
output = permuted.reshape(*attn_bias.shape[:-2], n_queries, num_splits * E)
return output
def rearrange_to_dense_trunk(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
n_queries: int,
n_keys: int,
attn_bias: Optional[torch.Tensor] = None,
inf: float = 1e10,
) -> tuple[Union[torch.Tensor, int]]:
"""Rearrange q/k/v/bias into blocked tensors for local attention.
Args:
q (torch.Tensor): query tensor
[..., n_q, d]
k (torch.Tensor): key tensor
[..., n_kv, d]
v (torch.Tensor): value tensor
[..., n_kv, d]
attn_bias (torch.Tensor, optional): attention bias
[..., n_q, n_kv] or None
n_queries (int, optional): local window size of query tensor.
n_keys (int, optional): local window size of key/value tensor.
inf (float, optional): used for attention masking. Defaults to 1e10.
Returns:
tuple[Union[torch.Tensor, int]]:
q_trunked
[..., n_trunks, n_queries, d]
k_trunked / v_trunked
[..., n_trunks, n_keys, d]
attn_bias_trunked: padded position filled with -inf
[..., n_trunks, n_queries, n_keys]
q_pad_length: query padded dimension
"""
assert n_keys >= n_queries
assert n_queries & 0x01 == 0
assert n_keys & 0x01 == 0
n, d = q.shape[-2:]
q_trunked, kv_trunked, padding_info = rearrange_qk_to_dense_trunk(
q=q,
k=[k, v],
dim_q=-2,
dim_k=[-2, -2],
n_queries=n_queries,
n_keys=n_keys,
compute_mask=False,
)
q_pad_length, pad_left, pad_right = (
padding_info["q_pad"],
padding_info["k_pad_left"],
padding_info["k_pad_right"],
)
# Padded_width = n + pad_left + pad_right
if attn_bias is None:
attn_bias = q.new_zeros(
*(1,) * len(q.shape[:-2]), n + q_pad_length, n + pad_left + pad_right
)
attn_bias[..., :n, 0:pad_left] = -inf
attn_bias[..., :n, pad_left + n : :] = -inf
attn_bias[..., n::, :] = -inf
else:
attn_bias = F.pad(attn_bias, (pad_left, pad_right, 0, q_pad_length), value=-inf)
concat_split_data = optimized_concat_split(attn_bias, n_queries)
attn_bias_trunked = concat_split_data.unfold(
-1, n_keys, attn_bias.shape[-1] + n_queries
).transpose(-2, -3)
return q_trunked, kv_trunked[0], kv_trunked[1], attn_bias_trunked, q_pad_length
def _local_attention(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
n_queries: int,
n_keys: int,
attn_bias: Optional[torch.Tensor] = None,
trunked_attn_bias: Optional[torch.Tensor] = None,
inf: float = 1e10,
use_efficient_implementation: bool = False,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
) -> torch.Tensor:
"""Local attention
Args:
q (torch.Tensor): query tensor
[..., Q, d]
k (torch.Tensor): key tensor
[..., K, d]
v (torch.Tensor): value tensor
[..., K, d]
n_queries (int): local window size of query.
n_keys (int): local window size of key/value.
attn_bias (torch.Tensor, optional): the input biases for attention. Defaults to None.
[..., Q, K]
trunked_attn_bias (torch.Tensor, optional): the input biases where shape has been rearranged to dense trunks. Defaults to None.
[..., n_trunks, n_queries, n_keys]
inf (float): inf number used for attention bias. Defaults to 1e10.
use_efficient_implementation (bool): whether to use the torch.nn.functional.scaled_dot_product_attention, Defaults to False.
Returns:
torch.Tensor: standard attention output
[..., Q, d]
"""
assert q.shape == k.shape == v.shape # local attention doesn't make sense if Q != K
# Prepare for attention qkv, q: [..., n_trunks, n_queries, d], kv: [..., n_trunks, n_keys, d]
# Rerrange to dense trunks
# q: [*, n, d] -> [*, n_trunks, n_queries, d]
# kv: [*, n, d] -> [*, n_trunks, n_keys, d]
# attn_bias: [*, n, d] -> [*, n_trunks, n_queries, n_keys]
q_trunked, k_trunked, v_trunked, attn_bias_trunked, q_pad_length = (
rearrange_to_dense_trunk(
q=q,
k=k,
v=v,
n_queries=n_queries,
n_keys=n_keys,
attn_bias=attn_bias,
inf=inf,
)
)
# Apply attention
# [..., n_trunks, n_queries, d]
if trunked_attn_bias is not None:
attn_bias_trunked = attn_bias_trunked + trunked_attn_bias
if chunk_size is not None:
attn_inputs = {
"q": q_trunked,
"k": k_trunked,
"v": v_trunked,
"attn_bias": attn_bias_trunked,
}
out = chunk_layer(
partial(
_attention,
use_efficient_implementation=use_efficient_implementation,
inplace_safe=inplace_safe,
),
attn_inputs,
chunk_size=chunk_size,
no_batch_dims=len(attn_bias_trunked.shape[:-2]),
_out=None,
)
else:
out = _attention(
q=q_trunked,
k=k_trunked,
v=v_trunked,
attn_bias=attn_bias_trunked,
use_efficient_implementation=use_efficient_implementation,
inplace_safe=inplace_safe,
)
# Revert back to orignal shape and remove q_pad_length
# [..., n_trunks, n_queries, d] -> [..., n_trunks * n_queries, d] -> [..., n, d]
out = out.reshape(*out.shape[:-3], -1, out.shape[-1])
if q_pad_length > 0:
out = out[..., :-q_pad_length, :]
return out
def create_local_attn_bias(
n: int, n_queries: int, n_keys: int, inf: float = 1e10, device: torch.device = None
) -> torch.Tensor:
"""Create local attention bias based on query window n_queries and kv window n_keys.
Args:
n (int): the length of quiries
n_queries (int): window size of quiries
n_keys (int): window size of keys/values
inf (float, optional): the inf to mask attention. Defaults to 1e10.
device (torch.device, optional): cuda|cpu|None. Defaults to None.
Returns:
torch.Tensor: the diagonal-like global attention bias
"""
n_trunks = int(math.ceil(n / n_queries))
padded_n = n_trunks * n_queries
attn_mask = torch.zeros(padded_n, padded_n, device=device)
for block_index in range(0, n_trunks):
i = block_index * n_queries
j1 = max(0, n_queries * block_index - (n_keys - n_queries) // 2)
j2 = n_queries * block_index + (n_queries + n_keys) // 2
attn_mask[i : i + n_queries, j1:j2] = 1.0
attn_bias = (1 - attn_mask) * -inf
return attn_bias.to(device=device)[:n, :n]
class Attention(nn.Module):
"""Standard multi-head attention
Ref to openfold:
https://github.com/aqlaboratory/openfold/blob/feb45a521e11af1db241a33d58fb175e207f8ce0/openfold/model/primitives.py#L340
"""
def __init__(
self,
c_q: int,
c_k: int,
c_v: int,
c_hidden: int,
num_heads: int,
gating: bool = True,
q_linear_bias: bool = True,
local_attention_method: str = "global_attention_with_bias",
use_efficient_implementation: bool = False,
zero_init: bool = True,
) -> None:
"""
Args:
c_q (int): Input dimension of query data
c_k (int): Input dimension of key data
c_v (int): Input dimension of value data
c_hidden (int): Per-head hidden dimension
num_heads (int): Number of attention heads
gating (bool, optional): Whether the output should be gated using query data. Defaults to True.
q_linear_bias (bool, optional): whether use Linear with bias as in AF3. Defaults to False.
local_attention_method (str, optional): local attention method, options:
- global_attention_with_bias: use full size global attention with sparse attention bias
- local_cross_attention: use local cross attention to minimize computation
use_efficient_implementation (bool): whether to use the torch.nn.functional.scaled_dot_product_attention, Defaults to False.
Notes:
if use_efficient_implementation == True, torch.nn.functional.scaled_dot_product_attention will
be used to compute attention efficiently
There are currently three supported implementations of scaled dot product attention:
1. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
2. Memory-Efficient Attention
3. A PyTorch implementation defined in C++ matching the above formulation
The function may call optimized kernels for improved performance when using the CUDA backend.
For all other backends, the PyTorch implementation will be used.All implementations are enabled by default.
Scaled dot product attention attempts to automatically select the most optimal implementation based on the inputs.
"""
super(Attention, self).__init__()
self.c_q = c_q
self.c_k = c_k
self.c_v = c_v
self.c_hidden = c_hidden
self.num_heads = num_heads
self.gating = gating
self.local_attention_method = local_attention_method
self.use_efficient_implementation = use_efficient_implementation
# DISCREPANCY: c_hidden is not the per-head channel dimension, as
# stated in the supplement, but the overall channel dimension.
if q_linear_bias:
# Attention in AF3
self.linear_q = Linear(
in_features=self.c_q, out_features=self.c_hidden * self.num_heads
)
else:
# Vanilla attention
self.linear_q = LinearNoBias(self.c_q, self.c_hidden * self.num_heads)
self.linear_k = LinearNoBias(self.c_k, self.c_hidden * self.num_heads)
self.linear_v = LinearNoBias(self.c_v, self.c_hidden * self.num_heads)
self.linear_o = LinearNoBias(self.c_hidden * self.num_heads, self.c_q)
self.linear_g = None
if self.gating:
self.linear_g = LinearNoBias(
self.c_q, self.c_hidden * self.num_heads, initializer="zeros"
)
self.sigmoid = nn.Sigmoid()
self.zero_init = zero_init
if self.zero_init:
# zero init the output layer
nn.init.zeros_(self.linear_o.weight)
def _prep_qkv(
self, q_x: torch.Tensor, kv_x: torch.Tensor, apply_scale: bool = True
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Prepare qkv
Args:
q_x (torch.Tensor): the input x for q
[..., c_q]
kv_x (torch.Tensor): the input x for kv
[..., c_k]
[..., c_v]
apply_scale (bool, optional): apply scale to dot product qk. Defaults to True.
Returns:
tuple[torch.Tensor, torch.Tensor, torch.Tensor]: the return q/k/v
# [..., H, Q/K/V, C_hidden]
"""
# [*, Q/K/V, H * C_hidden]
q = self.linear_q(q_x)
k = self.linear_k(kv_x)
v = self.linear_v(kv_x)
# [*, Q/K/V, H, C_hidden]
q = q.view(q.shape[:-1] + (self.num_heads, -1))
k = k.view(k.shape[:-1] + (self.num_heads, -1))
v = v.view(v.shape[:-1] + (self.num_heads, -1))
# [*, H, Q/K/V, C_hidden]
q = q.transpose(-2, -3)
k = k.transpose(-2, -3)
v = v.transpose(-2, -3)
if apply_scale:
q = q / math.sqrt(self.c_hidden)
return q, k, v
def _wrap_up(self, o: torch.Tensor, q_x: torch.Tensor) -> torch.Tensor:
"""
Finalize attention output with optional gating and output projection.
Args:
o (torch.Tensor): The multi-head attention output.
Shape: [..., Q, H, C_hidden]
q_x (torch.Tensor): The original query input for computing gate values.
Shape: [..., Q, c_q]
Returns:
torch.Tensor: The final attention output after gating and projection.
Shape: [..., Q, c_q]
"""
if self.linear_g is not None:
g = self.sigmoid(self.linear_g(q_x))
# [*, G/Q, H, C_hidden]
g = g.view(g.shape[:-1] + (self.num_heads, -1))
o = o * g
# [*, Q, H * C_hidden]
o = flatten_final_dims(o, num_dims=2)
# [*, Q, C_q]
o = self.linear_o(o)
return o
def forward(
self,
q_x: torch.Tensor,
kv_x: torch.Tensor,
attn_bias: Optional[torch.Tensor] = None,
trunked_attn_bias: Optional[torch.Tensor] = None,
n_queries: Optional[int] = None,
n_keys: Optional[int] = None,
inf: Optional[float] = 1e10,
inplace_safe: bool = False,
chunk_size: Optional[int] = None,
) -> torch.Tensor:
"""
Compute multi-head attention with optional local attention and gating.
Args:
q_x (torch.Tensor): Query input tensor.
Shape: [..., Q, C_q]
kv_x (torch.Tensor): Key/value input tensor.
Shape: [..., K, C_k]
attn_bias (torch.Tensor, optional): Attention bias for global attention.
Shape: [..., H, Q, K] or [..., Q, K]. Defaults to None.
trunked_attn_bias (torch.Tensor, optional): Pre-arranged attention bias
for local attention in dense trunk format. Only used with
local_cross_attention method.
Shape: [..., H, n_trunks, n_queries, n_keys] or
[..., n_trunks, n_queries, n_keys]. Defaults to None.
n_queries (int, optional): Local query window size. If provided with n_keys,
enables local attention. Defaults to None.
n_keys (int, optional): Local key/value window size. Defaults to None.
inf (float, optional): Value used for masking attention weights.
Defaults to 1e10.
inplace_safe (bool, optional): Whether it is safe to use inplace operations
for memory efficiency. Defaults to False.
chunk_size (Optional[int], optional): Chunk size for memory-efficient
processing in local attention. Defaults to None.
Returns:
torch.Tensor: Attention output with same shape as query input.
Shape: [..., Q, C_q]
"""
q, k, v = self._prep_qkv(q_x=q_x, kv_x=kv_x, apply_scale=True)
if attn_bias is not None:
if len(attn_bias.shape) == len(q.shape):
assert attn_bias.shape[:-2] == q.shape[:-2]
else:
assert len(attn_bias.shape) == len(q.shape) - 1
assert attn_bias.shape[:-2] == q.shape[:-3]
# Expand at head dim, got shape [..., 1, Q, K]
attn_bias = attn_bias.unsqueeze(dim=-3)
if trunked_attn_bias is not None:
# NOTE: trunked_attn_bias can only be used with "local_cross_attention" method
assert n_queries and n_keys
assert self.local_attention_method == "local_cross_attention"
if len(trunked_attn_bias.shape) == len(q.shape) + 1:
assert trunked_attn_bias.shape[:-3] == q.shape[:-2]
else:
assert len(trunked_attn_bias.shape) == len(q.shape)
# Expand at head dim, got shape [..., 1, n_trunks, n_queries, n_keys]
trunked_attn_bias = trunked_attn_bias.unsqueeze(dim=-4)
if n_queries and n_keys:
if self.local_attention_method == "global_attention_with_bias":
local_attn_bias = create_local_attn_bias(
q.shape[-2], n_queries, n_keys, inf=inf, device=q.device
)
# Expand to same shape as attn_bias
local_attn_bias = local_attn_bias.reshape(
(1,) * (len(q.shape[:-2])) + local_attn_bias.shape
)
if attn_bias is not None:
if inplace_safe:
local_attn_bias += attn_bias
else:
local_attn_bias = local_attn_bias + attn_bias
o = _attention(
q=q,
k=k,
v=v,
attn_bias=local_attn_bias,
use_efficient_implementation=self.use_efficient_implementation,
inplace_safe=inplace_safe,
)
elif self.local_attention_method == "local_cross_attention":
o = _local_attention(
q=q,
k=k,
v=v,
n_queries=n_queries,
n_keys=n_keys,
attn_bias=attn_bias,
trunked_attn_bias=trunked_attn_bias,
inf=inf,
use_efficient_implementation=self.use_efficient_implementation,
inplace_safe=inplace_safe,
chunk_size=chunk_size,
)
else:
raise ValueError(
f"Invalid local attention method: {self.local_attention_method}"
)
else:
o = _attention(
q=q,
k=k,
v=v,
attn_bias=attn_bias,
use_efficient_implementation=self.use_efficient_implementation,
inplace_safe=inplace_safe,
) # [*, H, Q, C_hidden]
o = o.transpose(-2, -3) # o: [*, Q, H, C_hidden]
o = self._wrap_up(o, q_x) # q_x: [*, Q, c_q]
return o
def gather_pair_embedding_in_dense_trunk(
x: torch.Tensor, idx_q: torch.Tensor, idx_k: torch.Tensor
) -> torch.Tensor:
"""
Selectively gather pair embeddings using two sets of block indices.
This function is used to extract specific token pair embeddings into
dense trunk format for local operations. It performs advanced indexing
to gather embeddings for pairs of tokens specified by idx_q and idx_k.
Args:
x (torch.Tensor): Input pair embeddings.
Shape: [..., N_token, N_token, d]
idx_q (torch.Tensor): Query token indices for each block.
Shape: [N_b, N_q]
idx_k (torch.Tensor): Key token indices for each block.
Shape: [N_b, N_k]
Returns:
torch.Tensor: Gathered pair embeddings in dense trunk format.
Shape: [..., N_b, N_q, N_k, d]
where output[..., b, i, j, :] = x[..., idx_q[b, i], idx_k[b, j], :]
"""
idx_q = idx_q.long()
idx_k = idx_k.long()
assert len(idx_q.shape) == len(idx_k.shape) == 2
# Get the shape parameters
N_b, N_q = idx_q.shape
N_k = idx_k.shape[1]
# Expand idx_q and idx_k to match the shape required for advanced indexing
idx_q_expanded = idx_q.unsqueeze(-1).expand(-1, -1, N_k)
idx_k_expanded = idx_k.unsqueeze(1).expand(-1, N_q, -1)
# Use advanced indexing to gather the desired elements
y = x[..., idx_q_expanded, idx_k_expanded, :]
return y
def broadcast_token_to_local_atom_pair(
z_token: torch.Tensor,
atom_to_token_idx: torch.Tensor,
n_queries: int,
n_keys: int,
compute_mask: bool = True,
) -> torch.Tensor:
"""Broadcast token pair embedding to atom pair embedding
Args:
z_token (torch.Tensor): token pair embedding
[..., N_token, N_token, d]
atom_to_token_idx (torch.Tensor): map atom idx to token idx
[N_atom]
Returns:
z_gathered_blocked (torch.Tensor): atom pair embedding, with local blocked shape
[..., n_trunks, n_queries, n_keys, d]
pad_mask (torch.Tensor):
[n_trunks, n_queries, n_keys]
q_pad_length (int)
"""
# [N_atom] -> [n_trunks, n_queries] and [n_trunks, n_keys]
atom_to_token_idx_q, atom_to_token_idx_k, pad_info = rearrange_qk_to_dense_trunk(
atom_to_token_idx,
atom_to_token_idx,
dim_q=-1,
dim_k=-1,
n_queries=n_queries,
n_keys=n_keys,
compute_mask=compute_mask,
)
z_gathered_blocked = gather_pair_embedding_in_dense_trunk(
z_token, idx_q=atom_to_token_idx_q, idx_k=atom_to_token_idx_k
)
return z_gathered_blocked, pad_info
# from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py
def drop_path(
x: torch.Tensor,
drop_prob: float = 0.0,
training: bool = False,
scale_by_keep: bool = True,
) -> torch.Tensor:
"""
Drop paths (Stochastic Depth) per sample for use in residual blocks.
This implements stochastic depth by randomly dropping entire samples
(all channels) rather than individual values. It's particularly effective
when applied to the main path of residual blocks.
Note: This is different from DropConnect, which drops individual connections.
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956
Args:
x (torch.Tensor): Input tensor.
Shape: [batch_size, ...]
drop_prob (float, optional): Probability of dropping a path.
Defaults to 0.0 (no dropping).
training (bool, optional): Whether in training mode. If False, returns input
unchanged. Defaults to False.
scale_by_keep (bool, optional): Whether to scale the output by keep probability
to maintain expected value. Defaults to True.
Returns:
torch.Tensor: Output tensor with same shape as input, with some samples
potentially zeroed out if drop_prob > 0 and training=True.
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(nn.Module):
"""
Drop paths (Stochastic Depth) module for residual networks.
This module wraps the drop_path function as a PyTorch nn.Module,
automatically using self.training to determine whether to apply dropping.
Typically used in the main path of residual blocks for regularization.
"""
def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True) -> None:
"""
Initialize DropPath module.
Args:
drop_prob (float, optional): Probability of dropping a path during training.
Defaults to 0.0 (no dropping).
scale_by_keep (bool, optional): Whether to scale output by keep probability.
Defaults to True.
"""
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Apply drop path to input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with same shape as input.
"""
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
def extra_repr(self) -> str:
"""
Extra representation string for this module.
Returns:
str: String representation showing drop_prob parameter.
"""
return f"drop_prob={round(self.drop_prob,3):0.3f}"
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