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# Copyright (c) 2025 FoundationVision
# SPDX-License-Identifier: MIT
import math
import torch
import torch.nn as nn
import torch.distributed as dist
import infinity.models.videovae.utils.diffdist.functional as distops
class ContextParallelUtils:
_CONTEXT_PARALLEL_GROUP = None
_CONTEXT_PARALLEL_SIZE = 0
_CONTEXT_PARALLEL_ON = False
"""
{
"cp_size": 2,
}
"""
CP_CONFIG = None
@staticmethod
def set_cp_on(on=True):
ContextParallelUtils._CONTEXT_PARALLEL_ON = on
@staticmethod
def cp_on():
return ContextParallelUtils._CONTEXT_PARALLEL_ON
@staticmethod
def get_cp_cfg():
return ContextParallelUtils.CP_CONFIG
@staticmethod
def is_cp_initialized():
if ContextParallelUtils._CONTEXT_PARALLEL_GROUP is None:
return False
else:
return True
@staticmethod
def initialize_context_parallel(cp_config:dict):
assert ContextParallelUtils._CONTEXT_PARALLEL_GROUP is None, "context parallel group is already initialized"
context_parallel_size = cp_config["cp_size"]
if context_parallel_size > 1:
ContextParallelUtils.CP_CONFIG = cp_config
else:
print(f"WARN: context parallel size must > 1 but got {context_parallel_size}")
return
ContextParallelUtils._CONTEXT_PARALLEL_SIZE = context_parallel_size
rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
for i in range(0, world_size, context_parallel_size):
ranks = range(i, i + context_parallel_size)
group = torch.distributed.new_group(ranks)
if rank in ranks:
ContextParallelUtils._CONTEXT_PARALLEL_GROUP = group
break
@staticmethod
def get_cp_group():
return ContextParallelUtils._CONTEXT_PARALLEL_GROUP
@staticmethod
def get_cp_size():
return ContextParallelUtils._CONTEXT_PARALLEL_SIZE
@staticmethod
def get_cp_world_size():
if ContextParallelUtils.is_cp_initialized():
world_size = torch.distributed.get_world_size()
return world_size // ContextParallelUtils._CONTEXT_PARALLEL_SIZE
else:
return 0
@staticmethod
def get_cp_rank():
if ContextParallelUtils.is_cp_initialized():
global_rank = torch.distributed.get_rank()
cp_rank = global_rank % ContextParallelUtils._CONTEXT_PARALLEL_SIZE
return cp_rank
else:
return 0
def get_cp_group_rank():
if ContextParallelUtils.is_cp_initialized():
rank = torch.distributed.get_rank()
cp_group_rank = rank // ContextParallelUtils._CONTEXT_PARALLEL_SIZE
return cp_group_rank
else:
return 0
def _gather_tensor_shape(local_ts):
cp_size = ContextParallelUtils.get_cp_size()
local_shape = torch.tensor(local_ts.shape, dtype=torch.int64, device=local_ts.device)
gathered_shapes = [torch.zeros(len(local_shape), dtype=torch.int64, device=local_ts.device) for _ in range(cp_size)]
dist.all_gather(gathered_shapes, local_shape, group=ContextParallelUtils._CONTEXT_PARALLEL_GROUP)
return [shape.tolist() for shape in gathered_shapes]
@torch.compiler.disable()
def dist_encoder_gather_result(res)->list:
cp_size = ContextParallelUtils.get_cp_size()
if cp_size < 2:
return res
shape_list = _gather_tensor_shape(res) # [[1,2,3,4],[x,x,x,x]] list of shapes on different rank
encs=[torch.zeros(s, device=res.device, dtype=res.dtype) for s in shape_list]
dist.barrier()
encs = distops.all_gather(encs, res, group=ContextParallelUtils._CONTEXT_PARALLEL_GROUP)
return encs
@torch.compiler.disable()
def dist_decoder_gather_result(res)->list:
cp_size = ContextParallelUtils.get_cp_size()
if cp_size < 2:
return res
shape_list = _gather_tensor_shape(res) # [[1,2,3,4],[x,x,x,x]] list of shapes on different rank
decs = [torch.zeros(s, device=res.device, dtype=res.dtype) for s in shape_list]
dist.barrier()
decs = distops.all_gather(decs, res, group=ContextParallelUtils._CONTEXT_PARALLEL_GROUP)
return decs
def _send_with_shape(local_ts, next_rank):
local_shape = torch.tensor(local_ts.shape, dtype=torch.int64, device=local_ts.device)
torch.distributed.send(local_shape.contiguous(), next_rank)
torch.distributed.send(local_ts.contiguous(), next_rank)
def _recv_with_shape(pre_rank):
device = torch.cuda.current_device() if torch.cuda.is_available() else torch.device('cpu')
shape = torch.zeros(5, dtype=torch.int64, device=device)
torch.distributed.recv(shape, pre_rank)
ts = torch.zeros(shape.tolist(), device=device)
torch.distributed.recv(ts, pre_rank)
return ts
@torch.compiler.disable()
def dist_conv_cache_send(conv_cache):
cp_rank = ContextParallelUtils.get_cp_rank()
global_rank = torch.distributed.get_rank()
cp_size = ContextParallelUtils.get_cp_size()
if cp_rank == cp_size - 1:
return
if conv_cache is None:
return
next_rank = global_rank + 1
_send_with_shape(conv_cache, next_rank)
@torch.compiler.disable()
def dist_conv_cache_recv():
cp_rank = ContextParallelUtils.get_cp_rank()
global_rank = torch.distributed.get_rank()
if cp_rank == 0:
return None
pre_rank = global_rank - 1
return _recv_with_shape(pre_rank)
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