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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint, checkpoint_sequential
from collections.abc import Iterable
from itertools import repeat
def _ntuple(n):
def parse(x):
if isinstance(x, Iterable) and not isinstance(x, str):
return x
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
def set_grad_checkpoint(model, use_fp32_attention=False, gc_step=1):
assert isinstance(model, nn.Module)
def set_attr(module):
module.grad_checkpointing = True
module.fp32_attention = use_fp32_attention
module.grad_checkpointing_step = gc_step
model.apply(set_attr)
def auto_grad_checkpoint(module, *args, **kwargs):
if getattr(module, 'grad_checkpointing', False):
if isinstance(module, Iterable):
gc_step = module[0].grad_checkpointing_step
return checkpoint_sequential(module, gc_step, *args, **kwargs)
else:
return checkpoint(module, *args, **kwargs)
return module(*args, **kwargs)
def checkpoint_sequential(functions, step, input, *args, **kwargs):
# Hack for keyword-only parameter in a python 2.7-compliant way
preserve = kwargs.pop('preserve_rng_state', True)
if kwargs:
raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs))
def run_function(start, end, functions):
def forward(input):
for j in range(start, end + 1):
input = functions[j](input, *args)
return input
return forward
if isinstance(functions, torch.nn.Sequential):
functions = list(functions.children())
# the last chunk has to be non-volatile
end = -1
segment = len(functions) // step
for start in range(0, step * (segment - 1), step):
end = start + step - 1
input = checkpoint(run_function(start, end, functions), input, preserve_rng_state=preserve)
return run_function(end + 1, len(functions) - 1, functions)(input)
def get_rel_pos(q_size, k_size, rel_pos):
"""
Get relative positional embeddings according to the relative positions of
query and key sizes.
Args:
q_size (int): size of query q.
k_size (int): size of key k.
rel_pos (Tensor): relative position embeddings (L, C).
Returns:
Extracted positional embeddings according to relative positions.
"""
max_rel_dist = int(2 * max(q_size, k_size) - 1)
# Interpolate rel pos if needed.
if rel_pos.shape[0] != max_rel_dist:
# Interpolate rel pos.
rel_pos_resized = F.interpolate(
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
size=max_rel_dist,
mode="linear",
)
rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
else:
rel_pos_resized = rel_pos
# Scale the coords with short length if shapes for q and k are different.
q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
return rel_pos_resized[relative_coords.long()]
def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size):
"""
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
Args:
attn (Tensor): attention map.
q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
Returns:
attn (Tensor): attention map with added relative positional embeddings.
"""
q_h, q_w = q_size
k_h, k_w = k_size
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
B, _, dim = q.shape
r_q = q.reshape(B, q_h, q_w, dim)
rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
attn = (
attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
).view(B, q_h * q_w, k_h * k_w)
return attn
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