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# Copyright (c) 2025 FoundationVision
# SPDX-License-Identifier: MIT
from torch.autograd import Function
import infinity.models.videovae.utils.diffdist.extra_collectives as dist_extra
import torch.distributed as dist
import torch
class ConsumeVariableFunc(Function):
@staticmethod
def forward(ctx, tensor_to_consume, set_ones_grad, *tensors_to_return):
ctx.save_for_backward(tensor_to_consume)
ctx.set_ones_grad = set_ones_grad
return tensors_to_return
@staticmethod
def backward(ctx, *grad_outputs):
tensor_to_consume, = ctx.saved_tensors
if ctx.set_ones_grad:
fake_grad = torch.ones_like(tensor_to_consume)
else:
fake_grad = torch.zeros_like(tensor_to_consume)
return (fake_grad, None) + grad_outputs
class SendFunc(Function):
@staticmethod
def forward(ctx, tensor, dst, group=dist.group.WORLD, tag=0):
ctx.save_for_backward(tensor)
ctx.dst = dst
ctx.group = group
ctx.tag = tag
dist.send(tensor, dst, group, tag)
return tensor.new_tensor([])
@staticmethod
def backward(ctx, grad_output):
tensor, = ctx.saved_tensors
# TODO: Add ctx.needs_input_grad check
grad_tensor = torch.zeros_like(tensor)
dist.recv(grad_tensor, ctx.dst, ctx.group, ctx.tag)
return grad_tensor, None, None, None
class RecvFunc(Function):
@staticmethod
def forward(ctx,
tensor,
src=None,
group=dist.group.WORLD,
tag=0,
inplace=True):
if not inplace:
tensor = torch.zeros_like(tensor).requires_grad_(False)
ctx.src = src
ctx.group = group
ctx.tag = tag
sender = dist.recv(tensor, src, group, tag)
if src:
assert sender == src
else:
ctx.src = sender
sender = torch.tensor(sender)
ctx.mark_non_differentiable(sender)
return tensor, sender
@staticmethod
def backward(ctx, grad_tensor, grad_sender):
dist.send(grad_tensor, ctx.src, ctx.group, ctx.tag)
return grad_tensor, None, None, None, None
class BroadcastFunc(Function):
@staticmethod
def forward(ctx, tensor, src, group=dist.group.WORLD, inplace=True):
ctx.src = src
ctx.group = group
if dist.get_rank(group) == src:
if not inplace:
with torch.no_grad():
tensor = tensor.clone().requires_grad_(False)
else:
if not inplace:
tensor = torch.zeros_like(tensor).requires_grad_(False)
dist.broadcast(tensor, src, group)
return tensor
@staticmethod
def backward(ctx, grad_output):
dist.reduce(grad_output,
ctx.src,
op=dist.ReduceOp.SUM,
group=ctx.group)
return grad_output, None, None, None
class AllReduceFunc(Function):
@staticmethod
def forward(ctx, i):
raise NotImplementedError
@staticmethod
def backward(ctx, grad_output):
raise NotImplementedError
class ReduceFunc(Function):
@staticmethod
def forward(ctx, i):
raise NotImplementedError
@staticmethod
def backward(ctx, grad_output):
raise NotImplementedError
class AllGatherFunc(Function):
@staticmethod
def forward(ctx, tensor, group, inplace, *gather_list):
ctx.save_for_backward(tensor)
ctx.group = group
gather_list = list(gather_list)
if not inplace:
gather_list = [torch.zeros_like(g) for g in gather_list]
dist.all_gather(gather_list, tensor, group)
return tuple(gather_list)
@staticmethod
def backward(ctx, *grads):
input, = ctx.saved_tensors
grad_out = torch.zeros_like(input)
dist_extra.reduce_scatter(grad_out, list(grads), group=ctx.group)
return (grad_out, None, None) + grads
class GatherFunc(Function):
@staticmethod
def forward(ctx, input, dst, group, inplace, *gather_list):
ctx.dst = dst
ctx.group = group
ctx.save_for_backward(input)
if dist.get_rank(group) == dst:
gather_list = list(gather_list)
if not inplace:
gather_list = [torch.zeros_like(g) for g in gather_list]
dist.gather(input, gather_list=gather_list, dst=dst, group=group)
return tuple(gather_list)
else:
dist.gather(input, [], dst=dst, group=group)
return input.new_tensor([])
@staticmethod
def backward(ctx, *grads):
input, = ctx.saved_tensors
grad_input = torch.zeros_like(input)
if dist.get_rank(ctx.group) == ctx.dst:
grad_outputs = list(grads)
dist.scatter(grad_input,
grad_outputs,
src=ctx.dst,
group=ctx.group)
return (grad_input, None, None, None) + grads
else:
dist.scatter(grad_input, [], src=ctx.dst, group=ctx.group)
return grad_input, None, None, None, None
class ScatterFunc(Function):
@staticmethod
def forward(ctx,
tensor,
src,
group=dist.group.WORLD,
inplace=True,
*scatter_list):
ctx.src = src
ctx.group = group
if not inplace:
tensor = torch.zeros_like(tensor)
if dist.get_rank(group) == src:
ctx.save_for_backward(*scatter_list)
scatter_list = list(scatter_list)
dist.scatter(tensor, scatter_list, src=src, group=group)
else:
dist.scatter(tensor, [], src=src, group=group)
return tensor
@staticmethod
def backward(ctx, grad_tensor):
if dist.get_rank(ctx.group) == ctx.src:
grad_outputs = [torch.zeros_like(g) for g in ctx.saved_tensors]
dist.gather(grad_tensor, grad_outputs, ctx.src, group=ctx.group)
return (grad_tensor, None, None, None) + tuple(grad_outputs)
else:
dist.gather(grad_tensor, [], ctx.src, group=ctx.group)
return grad_tensor, None, None, None, None
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