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Add test to verify virtual device usage metrics #4330

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merged 1 commit into from
Dec 15, 2022
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Add test to verify virtual device usage metrics #4330

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21 changes: 21 additions & 0 deletions test/test_xla_sharding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,10 @@
import numpy as np

import torch
from torch import nn
import torch_xla
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
import torch_xla.utils.utils as xu
import torch_xla.experimental.xla_sharding as xs
from torch_xla.experimental.xla_sharded_tensor import XLAShardedTensor
Expand Down Expand Up @@ -137,6 +139,25 @@ def test_mark_sharding(self):
dtype=torch.float,
device=xm.xla_device())))

def test_metrics_recorded(self):
met.clear_counters()
partition_spec = (0, 1)
xt1 = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8]],
dtype=torch.float,
device=xm.xla_device())
xs.mark_sharding(xt1, self._get_mesh((1, self.n_devices)), partition_spec)
self.assertIn("VirtualDeviceUsage", met.counter_names())
self.assertNotEqual(met.counter_value("VirtualDeviceUsage"), 0)
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@yeounoh yeounoh Dec 14, 2022

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This looks good to me. Could we add another test case showing that the model param sharding doesn't use virtual device?

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@steventk-g steventk-g Dec 15, 2022

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Added a test for sharding on model weights. It looks like virtual device will still be used to delay the transfer of the model weights in nn.Linear(128, 64).to(xm.xla_device()) until the sharding is applied.

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@yeounoh yeounoh Dec 15, 2022

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Nice!


def test_model_weight_metrics(self):
met.clear_counters()
partition_spec = (0, 1)
model = nn.Linear(128, 64).to(xm.xla_device())
xs.mark_sharding(model.weight, self._get_mesh((1, self.n_devices)),
partition_spec)
self.assertIn("VirtualDeviceUsage", met.counter_names())
self.assertNotEqual(met.counter_value("VirtualDeviceUsage"), 0)


if __name__ == '__main__':
test = unittest.main()
Expand Down