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from train_resnet_base import TrainResNetBase | ||
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import numpy as np | ||
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import torch | ||
import torch_xla.distributed.xla_multiprocessing as xmp | ||
import torch_xla.core.xla_model as xm | ||
import torch_xla.distributed.spmd as xs | ||
import torch_xla.distributed.parallel_loader as pl | ||
import torch_xla.utils.utils as xu | ||
from torch_xla import runtime as xr | ||
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# Enable the SPMD | ||
xr.use_spmd() | ||
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# More detailed examaple can be found in https://github.com/pytorch/xla/blob/master/test/spmd/test_train_spmd_imagenet.py | ||
# Check out our user guide in https://github.com/pytorch/xla/blob/master/docs/spmd.md | ||
class TrainResNetXLASpmdDDP(TrainResNetBase): | ||
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def __init__(self): | ||
super().__init__() | ||
# Shard along batch dimension only | ||
num_devices = xr.global_runtime_device_count() | ||
device_ids = np.arange(num_devices) | ||
mesh_shape = (num_devices,) | ||
mesh = xs.Mesh(device_ids, mesh_shape, ('data',)) | ||
# scale the batch size with num_devices since there will be only one | ||
# process that handles all runtime devices. | ||
self.batch_size *= num_devices | ||
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train_loader = xu.SampleGenerator( | ||
data=(torch.zeros(self.batch_size, 3, self.img_dim, self.img_dim), | ||
torch.zeros(self.batch_size, dtype=torch.int64)), | ||
sample_count=self.train_dataset_len // self.batch_size) | ||
self.train_device_loader = pl.MpDeviceLoader( | ||
train_loader, | ||
self.device, | ||
# Shard the input's batch dimension along the `data` axis, no sharding along other dimensions | ||
input_sharding=xs.ShardingSpec(mesh, ('data', None, None, None))) | ||
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if __name__ == '__main__': | ||
spmd_ddp = TrainResNetXLASpmdDDP() | ||
spmd_ddp.start_training() |