72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
import os
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import torch
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from datetime import timedelta
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class FakeBarrier:
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def wait(self):
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pass
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class FakeGroup:
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def __init__(self, rank, size):
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self._rank = rank
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self._size = size
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def allreduce(self, *args, **kwargs):
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return FakeBarrier()
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def allgather(self, inputs, local_tensor, **kwargs):
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assert (
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len(inputs[0]) == len(local_tensor) == 1
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), f"{len(inputs[0])} != {len(local_tensor)} != 1, and the FakeGroup is supposed to join on simple tensors"
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for input_ in inputs:
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input_[0].data = local_tensor[0].data
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return FakeBarrier()
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def barrier(self, *args, **kwargs):
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return FakeBarrier()
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def size(self):
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return self._size
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def rank(self):
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return self._rank
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def initialize_torch_distributed():
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rank = int(os.getenv("RANK", "0"))
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world_size = int(os.getenv("WORLD_SIZE", "1"))
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if torch.cuda.is_available():
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from torch.distributed import ProcessGroupNCCL
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# Set the device id.
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assert world_size <= torch.cuda.device_count(), "Each process is one gpu"
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device = rank % torch.cuda.device_count()
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torch.cuda.set_device(device)
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backend = "nccl"
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options = ProcessGroupNCCL.Options()
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options.is_high_priority_stream = True
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options._timeout = timedelta(seconds=60)
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else:
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backend = "gloo"
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options = None
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if world_size == 1:
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return FakeGroup(rank, world_size), rank, world_size
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else:
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if os.getenv("DEBUG", None) == "1":
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return FakeGroup(rank, world_size), rank, world_size
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# Call the init process.
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torch.distributed.init_process_group(
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backend=backend,
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world_size=world_size,
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rank=rank,
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timeout=timedelta(seconds=60),
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pg_options=options,
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)
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return torch.distributed.group.WORLD, rank, world_size
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