87 lines
3.8 KiB
Python
Executable File
87 lines
3.8 KiB
Python
Executable File
import importlib.util
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import torch
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import torch.distributed as dist
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try:
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# The pai_fuser is an internally developed acceleration package, which can be used on PAI.
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if importlib.util.find_spec("paifuser") is not None:
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import paifuser
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from paifuser.xfuser.core.distributed import (
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get_sequence_parallel_rank, get_sequence_parallel_world_size,
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get_sp_group, get_world_group, init_distributed_environment,
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initialize_model_parallel, model_parallel_is_initialized)
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from paifuser.xfuser.core.long_ctx_attention import \
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xFuserLongContextAttention
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print("Import PAI DiT Turbo")
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else:
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import xfuser
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from xfuser.core.distributed import (get_sequence_parallel_rank,
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get_sequence_parallel_world_size,
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get_sp_group, get_world_group,
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init_distributed_environment,
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initialize_model_parallel,
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model_parallel_is_initialized)
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from xfuser.core.long_ctx_attention import xFuserLongContextAttention
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print("Xfuser import sucessful")
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except Exception as ex:
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get_sequence_parallel_world_size = None
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get_sequence_parallel_rank = None
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xFuserLongContextAttention = None
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get_sp_group = None
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get_world_group = None
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init_distributed_environment = None
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initialize_model_parallel = None
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def set_multi_gpus_devices(ulysses_degree, ring_degree, classifier_free_guidance_degree=1):
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if ulysses_degree > 1 or ring_degree > 1 or classifier_free_guidance_degree > 1:
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if get_sp_group is None:
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raise RuntimeError("xfuser is not installed.")
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dist.init_process_group("nccl")
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print('parallel inference enabled: ulysses_degree=%d ring_degree=%d classifier_free_guidance_degree=% rank=%d world_size=%d' % (
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ulysses_degree, ring_degree, classifier_free_guidance_degree, dist.get_rank(),
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dist.get_world_size()))
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assert dist.get_world_size() == ring_degree * ulysses_degree * classifier_free_guidance_degree, \
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"number of GPUs(%d) should be equal to ring_degree * ulysses_degree * classifier_free_guidance_degree." % dist.get_world_size()
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init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size())
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initialize_model_parallel(sequence_parallel_degree=ring_degree * ulysses_degree,
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classifier_free_guidance_degree=classifier_free_guidance_degree,
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ring_degree=ring_degree,
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ulysses_degree=ulysses_degree)
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# device = torch.device("cuda:%d" % dist.get_rank())
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device = torch.device(f"cuda:{get_world_group().local_rank}")
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print('rank=%d device=%s' % (get_world_group().rank, str(device)))
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else:
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device = "cuda"
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return device
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def sequence_parallel_chunk(x, dim=1):
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if get_sequence_parallel_world_size is None or not model_parallel_is_initialized():
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return x
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sp_world_size = get_sequence_parallel_world_size()
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if sp_world_size <= 1:
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return x
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sp_rank = get_sequence_parallel_rank()
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sp_group = get_sp_group()
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if x.size(1) % sp_world_size != 0:
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raise ValueError(f"Dim 1 of x ({x.size(1)}) not divisible by SP world size ({sp_world_size})")
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chunks = torch.chunk(x, sp_world_size, dim=1)
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x = chunks[sp_rank]
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return x
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def sequence_parallel_all_gather(x, dim=1):
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if get_sequence_parallel_world_size is None or not model_parallel_is_initialized():
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return x
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sp_world_size = get_sequence_parallel_world_size()
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if sp_world_size <= 1:
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return x # No gathering needed
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sp_group = get_sp_group()
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gathered_x = sp_group.all_gather(x, dim=dim)
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return gathered_x |