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# Copyright (c) 2025 FoundationVision
# SPDX-License-Identifier: MIT
import infinity.models.videovae.utils.diffdist.functional as distops
import torch.distributed as dist
import torch
import infinity.models.videovae.utils.diffdist.extra_collectives as extra_comm
def test_reduce_scatter():
if dist.get_rank() == 0:
print("REDUCE_SCATTER TEST\n")
x = torch.arange(dist.get_world_size()).float().split(1)
buff = torch.tensor(0.)
extra_comm.reduce_scatter(buff, x)
print(dist.get_rank(), x)
print(dist.get_rank(), buff)
dist.barrier()
if dist.get_rank() == 0:
print('-' * 50)
def test_all_gather():
if dist.get_rank() == 0:
print("ALL GATHER TEST\n")
dist.barrier()
x = torch.tensor(3., requires_grad=True)
y = (dist.get_rank() + 1) * x
print(dist.get_rank(), "Sending y:", y)
z = distops.all_gather(list(torch.zeros(dist.get_world_size())),
y,
next_backprop=None,
inplace=True)
print(dist.get_rank(), "Received tensor:", z)
l = torch.sum(torch.stack(z))
l = l * (dist.get_rank() + 1)
l.backward()
print(dist.get_rank(), "Gradient with MPI:", x.grad)
dist.barrier()
if dist.get_rank() == 0:
print()
x = [
torch.tensor(3., requires_grad=True)
for i in range(dist.get_world_size())
]
res = []
for i in range(1, dist.get_world_size() + 1):
res.append(i * x[i - 1])
res2 = []
for i in range(dist.get_world_size()):
temp = []
for j in range(dist.get_world_size()):
temp.append(torch.clone(res[j]))
res2.append(temp)
l_s = [torch.sum(torch.stack(i)) for i in res2]
final = [(i + 1) * k for i, k in enumerate(l_s)]
for i in range(dist.get_world_size() - 1):
final[i].backward(retain_graph=True)
final[-1].backward()
for i, x_i in enumerate(x):
print(i, "Gradient in single process:", x_i.grad)
print('-' * 50)
def test_scatter():
if dist.get_rank() == 0:
print("SCATTER TEST\n")
x = [
torch.tensor(3., requires_grad=True)
for i in range(dist.get_world_size())
]
y = [2 * x_i for x_i in x]
print("Sending y:", y)
buffer = torch.tensor(0.)
z = distops.scatter(buffer, y, src=0, inplace=False)
else:
buffer = torch.tensor(0., requires_grad=True)
z = distops.scatter(buffer, src=0, inplace=False)
print(dist.get_rank(), "Received tensor:", z)
# Computation
k = (dist.get_rank() + 1) * z
k.backward()
if dist.get_rank() == 0:
print("Gradient with MPI:", [x_i.grad for x_i in x])
if dist.get_rank() == 0:
print()
x = [
torch.tensor(3., requires_grad=True)
for i in range(dist.get_world_size())
]
y = [2 * x_i for x_i in x]
res = []
for i in range(dist.get_world_size()):
res.append((i + 1) * y[i])
for i, k in enumerate(res):
k.backward()
print("Gradient in single process:", [x_i.grad for x_i in x])
dist.barrier()
if dist.get_rank() == 0:
print('-' * 50)
def test_gather():
if dist.get_rank() == 0:
print("GATHER TEST\n")
dist.barrier()
x = torch.tensor(3., requires_grad=True)
y = (dist.get_rank() + 1) * x
print(dist.get_rank(), "Sending y:", y)
if dist.get_rank() == 0:
z = distops.gather(y,
torch.zeros(dist.get_world_size()).split(1),
dst=0,
next_backprop=None,
inplace=True)
print(dist.get_rank(), "Received tensor:", z)
l = torch.sum(torch.stack(z))
l.backward()
else:
dummy = distops.gather(y, dst=0, next_backprop=None, inplace=True)
dummy.backward(torch.tensor([]))
print(dist.get_rank(), "Gradient with MPI:", x.grad)
dist.barrier()
if dist.get_rank() == 0:
print()
x = [
torch.tensor(3., requires_grad=True)
for i in range(dist.get_world_size())
]
res = []
for i in range(1, dist.get_world_size() + 1):
res.append(i * x[i - 1])
z = torch.stack(res)
l = torch.sum(z)
l.backward()
for i, x_i in enumerate(x):
print(i, "Gradient in single process:", x_i.grad)
print('-' * 50)
def test_broadcast():
if dist.get_rank() == 0:
print("BROADCAST TEST\n")
x = torch.tensor(3., requires_grad=True)
y = 2 * x
print(dist.get_rank(), "Sending y:", y)
z = distops.broadcast(y, src=0, inplace=False)
print(dist.get_rank(), "Received tensor:", z)
# Computation
k = 3 * z
k.backward()
print("Gradient with MPI:", x.grad)
print()
x = torch.tensor(3., requires_grad=True)
y = 2 * x
res = [3 * y]
for i in range(1, dist.get_world_size()):
res.append(9 * y)
for i, k in enumerate(res):
if i == (len(res) - 1):
k.backward()
else:
k.backward(retain_graph=True)
print("Gradient in single process:", x.grad)
else:
x = torch.tensor(5., requires_grad=True)
y = 7 * x
buffer = torch.tensor(0.)
z = distops.broadcast(buffer, src=0, next_backprop=y)
print(dist.get_rank(), "Received tensor:", z)
k = 9 * z
k.backward()
print(dist.get_rank(), "Grad of disconnected part:", x.grad)
dist.barrier()
if dist.get_rank() == 0:
print('-' * 50)
def test_consume_variable():
x = torch.tensor(5., requires_grad=True)
y = 2 * x
z = 3 * y
j = 4 * y
z = distops.consume_variable(j, [z], set_ones_grad=True)[0]
print(z)
z.backward()
print(x.grad)
print()
x = torch.tensor(5., requires_grad=True)
y = 2 * x
z = 3 * y
j = 4 * y
z.backward(retain_graph=True)
j.backward()
print(x.grad)
def test_send_recv():
if dist.get_rank() == 0:
print("SEND/RECV TEST\n")
x = torch.tensor(3., requires_grad=True)
y = 2 * x
print("Before sending y:", y)
connector = distops.send(y, dst=1)
# Computation happens in process 1
buffer = torch.tensor(0.)
z, _ = distops.recv(buffer, src=1, next_backprop=connector)
print("After receiving:", z)
k = 3 * z
k.backward()
print("Gradient with MPI:", x.grad)
print()
x = torch.tensor(3., requires_grad=True)
y = 2 * x
l = y * 10
k = 3 * l
k.backward()
print("Gradient in single process:", x.grad)
print('-' * 50)
elif dist.get_rank() == 1:
buffer = torch.tensor(0., requires_grad=True)
y, _ = distops.recv(buffer, src=0)
l = y * 10
connector = distops.send(l, dst=0)
connector.backward(torch.tensor([]))
if __name__ == '__main__':
dist.init_process_group('mpi')
print(f'I am {dist.get_rank()}')
dist.barrier()
if dist.get_rank() == 0:
print('-' * 50)
if dist.get_rank() == 0:
print("EXTRA COLLECTIVES")
test_reduce_scatter()
if dist.get_rank() == 0:
print('-' * 50)
test_send_recv()
test_broadcast()
test_gather()
test_scatter()
test_all_gather()
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