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87b732d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 | """Copyright (c) Microsoft Corporation. Licensed under the MIT license.
Basic blocks for the Perceiver architecture.
The code borrows elements from the following files:
https://github.com/lucidrains/perceiver-pytorch/blob/main/perceiver_pytorch/perceiver_pytorch.py
https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
These files are licenced under respectively the following two licences:
MIT License
Copyright (c) 2021 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
MIT License
Copyright (c) 2023 Anas Awadalla, Irena Gao, Joshua Gardner, Jack Hessel, Yusuf
Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Jenia Jitsev,
Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from .aurora_util import fp16_safe_scaled_dot_product_attention
__all__ = ["MLP", "PerceiverResampler"]
class MLP(nn.Module):
"""A simple one-hidden-layer MLP."""
def __init__(self, dim: int, hidden_features: int, dropout: float = 0.0) -> None:
"""Initialise.
Args:
dim (int): Input dimensionality.
hidden_features (int): Width of the hidden layer.
dropout (float, optional): Drop-out rate. Defaults to no drop-out.
"""
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_features),
nn.GELU(),
nn.Linear(hidden_features, dim),
nn.Dropout(dropout),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Run the MLP."""
return self.net(x)
class PerceiverAttention(nn.Module):
"""Cross attention module from the Perceiver architecture."""
def __init__(
self,
latent_dim: int,
context_dim: int,
head_dim: int = 64,
num_heads: int = 8,
ln_k_q: bool = False,
) -> None:
"""Initialise.
Args:
latent_dim (int): Dimensionality of the latent features given as input.
context_dim (int): Dimensionality of the context features also given as input.
head_dim (int): Attention head dimensionality.
num_heads (int): Number of heads.
ln_k_q (bool): Apply an extra layer norm. to the keys and queries.
"""
super().__init__()
self.num_heads = num_heads
self.head_dim = head_dim
self.inner_dim = head_dim * num_heads
self.to_q = nn.Linear(latent_dim, self.inner_dim, bias=False)
self.to_kv = nn.Linear(context_dim, self.inner_dim * 2, bias=False)
self.to_out = nn.Linear(self.inner_dim, latent_dim, bias=False)
self.use_fp16_safe_attention = False
if ln_k_q:
self.ln_k = nn.LayerNorm(num_heads * head_dim)
self.ln_q = nn.LayerNorm(num_heads * head_dim)
else:
self.ln_k = lambda x: x
self.ln_q = lambda x: x
def forward(self, latents: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
"""Run the cross-attention module.
Args:
latents (:class:`torch.Tensor`): Latent features of shape `(B, L1, Latent_D)`
where typically `L1 < L2` and `Latent_D <= Context_D`. `Latent_D` is equal to
`self.latent_dim`.
x (:class:`torch.Tensor`): Context features of shape `(B, L2, Context_D)`.
Returns:
:class:`torch.Tensor`: Latent values of shape `(B, L1, Latent_D)`.
"""
h = self.num_heads
q = self.to_q(latents) # (B, L1, D2) to (B, L1, D)
k, v = self.to_kv(x).chunk(2, dim=-1) # (B, L2, D1) to twice (B, L2, D)
# Apply LN before (!) splitting the heads.
k = self.ln_k(k)
q = self.ln_q(q)
q, k, v = map(lambda t: rearrange(t, "b l (h d) -> b h l d", h=h), (q, k, v))
sdpa = (
fp16_safe_scaled_dot_product_attention
if self.use_fp16_safe_attention
else F.scaled_dot_product_attention
)
out = sdpa(q, k, v)
out = rearrange(out, "B H L1 D -> B L1 (H D)") # (B, L1, D)
return self.to_out(out) # (B, L1, Latent_D)
class PerceiverResampler(nn.Module):
"""Perceiver Resampler module from the Flamingo paper."""
def __init__(
self,
latent_dim: int,
context_dim: int,
depth: int = 1,
head_dim: int = 64,
num_heads: int = 16,
mlp_ratio: float = 4.0,
drop: float = 0.0,
residual_latent: bool = True,
ln_eps: float = 1e-5,
ln_k_q: bool = False,
) -> None:
"""Initialise.
Args:
latent_dim (int): Dimensionality of the latent features given as input.
context_dim (int): Dimensionality of the context features also given as input.
depth (int, optional): Number of attention layers.
head_dim (int, optional): Attention head dimensionality. Defaults to `64`.
num_heads (int, optional): Number of heads. Defaults to `16`
mlp_ratio (float, optional): Rimensionality of the hidden layer divided by that of the
input for all MLPs. Defaults to `4.0`.
drop (float, optional): Drop-out rate. Defaults to no drop-out.
residual_latent (bool, optional): Use residual attention w.r.t. the latent features.
Defaults to `True`.
ln_eps (float, optional): Epsilon in the layer normalisation layers. Defaults to
`1e-5`.
ln_k_q (bool, optional): Apply an extra layer norm. to the keys and queries of the first
resampling layer. Defaults to `False`.
"""
super().__init__()
self.residual_latent = residual_latent
self.layers = nn.ModuleList([])
mlp_hidden_dim = int(latent_dim * mlp_ratio)
for i in range(depth):
self.layers.append(
nn.ModuleList(
[
PerceiverAttention(
latent_dim=latent_dim,
context_dim=context_dim,
head_dim=head_dim,
num_heads=num_heads,
ln_k_q=ln_k_q if i == 0 else False,
),
MLP(dim=latent_dim, hidden_features=mlp_hidden_dim, dropout=drop),
nn.LayerNorm(latent_dim, eps=ln_eps),
nn.LayerNorm(latent_dim, eps=ln_eps),
]
)
)
def forward(self, latents: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
"""Run the module.
Args:
latents (:class:`torch.Tensor`): Latent features of shape `(B, L1, D1)`.
x (:class:`torch.Tensor`): Context features of shape `(B, L2, D1)`.
Returns:
torch.Tensor: Latent features of shape `(B, L1, D1)`.
"""
for attn, ff, ln1, ln2 in self.layers:
# We use post-res-norm like in Swin v2 and most Transformer architectures these days.
# This empirically works better than the pre-norm used in the original Perceiver.
attn_out = ln1(attn(latents, x))
# HuggingFace suggests using non-residual attention in Perceiver might work better when
# the semantics of the query and the output are different:
#
# https://github.com/huggingface/transformers/blob/v4.35.2/src/transformers/models/perceiver/modeling_perceiver.py#L398
#
latents = attn_out + latents if self.residual_latent else attn_out
if not self.training:
# Save memory in inference by not keeping intermediate activations
del attn_out
latents = ln2(ff(latents)) + latents
return latents
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