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# --------------------------------------------------------
# Reversible Column Networks
# Copyright (c) 2022 Megvii Inc.
# Licensed under The Apache License 2.0 [see LICENSE for details]
# Written by Yuxuan Cai
# --------------------------------------------------------
import torch
from typing import Any, Iterable, List, Tuple, Callable
import torch.distributed as dist
def get_gpu_states(fwd_gpu_devices) -> Tuple[List[int], List[torch.Tensor]]:
# This will not error out if "arg" is a CPU tensor or a non-tensor type because
# the conditionals short-circuit.
fwd_gpu_states = []
for device in fwd_gpu_devices:
with torch.cuda.device(device):
fwd_gpu_states.append(torch.cuda.get_rng_state())
return fwd_gpu_states
def get_gpu_device(*args):
fwd_gpu_devices = list(set(arg.get_device() for arg in args
if isinstance(arg, torch.Tensor) and arg.is_cuda))
return fwd_gpu_devices
def set_device_states(fwd_cpu_state, devices, states) -> None:
torch.set_rng_state(fwd_cpu_state)
for device, state in zip(devices, states):
with torch.cuda.device(device):
torch.cuda.set_rng_state(state)
def detach_and_grad(inputs: Tuple[Any, ...]) -> Tuple[torch.Tensor, ...]:
if isinstance(inputs, tuple):
out = []
for inp in inputs:
if not isinstance(inp, torch.Tensor):
out.append(inp)
continue
x = inp.detach()
x.requires_grad = True
out.append(x)
return tuple(out)
else:
raise RuntimeError(
"Only tuple of tensors is supported. Got Unsupported input type: ", type(inputs).__name__)
def get_cpu_and_gpu_states(gpu_devices):
return torch.get_rng_state(), get_gpu_states(gpu_devices)
class ReverseFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, run_functions, alpha, *args):
l0, l1, l2, l3 = run_functions
alpha0, alpha1, alpha2, alpha3 = alpha
ctx.run_functions = run_functions
ctx.alpha = alpha
ctx.preserve_rng_state = True
ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
"dtype": torch.get_autocast_gpu_dtype(),
"cache_enabled": torch.is_autocast_cache_enabled()}
ctx.cpu_autocast_kwargs = {"enabled": torch.is_autocast_cpu_enabled(),
"dtype": torch.get_autocast_cpu_dtype(),
"cache_enabled": torch.is_autocast_cache_enabled()}
assert len(args) == 5
[x, c0, c1, c2, c3] = args
if type(c0) == int:
ctx.first_col = True
else:
ctx.first_col = False
with torch.no_grad():
gpu_devices = get_gpu_device(*args)
ctx.gpu_devices = gpu_devices
ctx.cpu_states_0, ctx.gpu_states_0 = get_cpu_and_gpu_states(gpu_devices)
c0 = l0(x, c1) + c0*alpha0
ctx.cpu_states_1, ctx.gpu_states_1 = get_cpu_and_gpu_states(gpu_devices)
c1 = l1(c0, c2) + c1*alpha1
ctx.cpu_states_2, ctx.gpu_states_2 = get_cpu_and_gpu_states(gpu_devices)
c2 = l2(c1, c3) + c2*alpha2
ctx.cpu_states_3, ctx.gpu_states_3 = get_cpu_and_gpu_states(gpu_devices)
c3 = l3(c2, None) + c3*alpha3
ctx.save_for_backward(x, c0, c1, c2, c3)
return x, c0, c1 ,c2, c3
@staticmethod
def backward(ctx, *grad_outputs):
x, c0, c1, c2, c3 = ctx.saved_tensors
l0, l1, l2, l3 = ctx.run_functions
alpha0, alpha1, alpha2, alpha3 = ctx.alpha
gx_right, g0_right, g1_right, g2_right, g3_right = grad_outputs
(x, c0, c1, c2, c3) = detach_and_grad((x, c0, c1, c2, c3))
with torch.enable_grad(), \
torch.random.fork_rng(devices=ctx.gpu_devices, enabled=ctx.preserve_rng_state), \
torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs), \
torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):
g3_up = g3_right
g3_left = g3_up*alpha3 ##shortcut
set_device_states(ctx.cpu_states_3, ctx.gpu_devices, ctx.gpu_states_3)
oup3 = l3(c2, None)
torch.autograd.backward(oup3, g3_up, retain_graph=True)
with torch.no_grad():
c3_left = (1/alpha3)*(c3 - oup3) ## feature reverse
g2_up = g2_right+ c2.grad
g2_left = g2_up*alpha2 ##shortcut
(c3_left,) = detach_and_grad((c3_left,))
set_device_states(ctx.cpu_states_2, ctx.gpu_devices, ctx.gpu_states_2)
oup2 = l2(c1, c3_left)
torch.autograd.backward(oup2, g2_up, retain_graph=True)
c3_left.requires_grad = False
cout3 = c3_left*alpha3 ##alpha3 update
torch.autograd.backward(cout3, g3_up)
with torch.no_grad():
c2_left = (1/alpha2)*(c2 - oup2) ## feature reverse
g3_left = g3_left + c3_left.grad if c3_left.grad is not None else g3_left
g1_up = g1_right+c1.grad
g1_left = g1_up*alpha1 ##shortcut
(c2_left,) = detach_and_grad((c2_left,))
set_device_states(ctx.cpu_states_1, ctx.gpu_devices, ctx.gpu_states_1)
oup1 = l1(c0, c2_left)
torch.autograd.backward(oup1, g1_up, retain_graph=True)
c2_left.requires_grad = False
cout2 = c2_left*alpha2 ##alpha2 update
torch.autograd.backward(cout2, g2_up)
with torch.no_grad():
c1_left = (1/alpha1)*(c1 - oup1) ## feature reverse
g0_up = g0_right + c0.grad
g0_left = g0_up*alpha0 ##shortcut
g2_left = g2_left + c2_left.grad if c2_left.grad is not None else g2_left ## Fusion
(c1_left,) = detach_and_grad((c1_left,))
set_device_states(ctx.cpu_states_0, ctx.gpu_devices, ctx.gpu_states_0)
oup0 = l0(x, c1_left)
torch.autograd.backward(oup0, g0_up, retain_graph=True)
c1_left.requires_grad = False
cout1 = c1_left*alpha1 ##alpha1 update
torch.autograd.backward(cout1, g1_up)
with torch.no_grad():
c0_left = (1/alpha0)*(c0 - oup0) ## feature reverse
gx_up = x.grad ## Fusion
g1_left = g1_left + c1_left.grad if c1_left.grad is not None else g1_left ## Fusion
c0_left.requires_grad = False
cout0 = c0_left*alpha0 ##alpha0 update
torch.autograd.backward(cout0, g0_up)
if ctx.first_col:
return None, None, gx_up, None, None, None, None
else:
return None, None, gx_up, g0_left, g1_left, g2_left, g3_left
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