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# Owner(s): ["oncall: distributed"]
import os
import unittest
import torch
import torch.nn as nn
from torch.distributed._tools import MemoryTracker
from torch.testing._internal.common_utils import run_tests, TestCase
class TestMemoryTracker(TestCase):
@unittest.skipIf(not torch.accelerator.is_available(), "no accelerator")
def test_local_model(self):
"""
Minimal test case to check the memory tracker can collect the expected
memory stats at operator level, as well as can print the summary result
without crash.
"""
device = torch.accelerator.current_accelerator()
# Create a model with a hierarchy of modules
torch.manual_seed(0)
model = nn.Sequential(
nn.Sequential(
nn.Conv2d(3, 64, kernel_size=(3, 3), padding=(1, 1), bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=False),
nn.AdaptiveAvgPool2d(output_size=(1, 1)),
),
nn.Flatten(start_dim=1),
nn.Sequential(nn.Linear(64, 2), nn.ReLU(inplace=True)),
).to(device)
# Run one iteration of forward and backward pass
tracker = MemoryTracker()
tracker.start_monitor(model)
x = torch.randn(size=(2, 3, 224, 224), device=device)
# torch.LongTensor expects cpu device type, not gpu device type in
# constructor, so calling .to() outside constructor here.
target = torch.LongTensor([0, 1]).to(device)
criterion = nn.CrossEntropyLoss()
criterion(model(x), target).backward()
self.assertTrue(len(tracker._hooks) > 0)
tracker.stop()
self.assertTrue(len(tracker._hooks) == 0)
path = "memory.trace"
tracker.save_stats(path)
tracker.load(path)
tracker.summary()
if os.path.exists(path):
os.remove(path)
self.assertTrue(tracker._op_index > 0)
self.assertTrue(len(tracker._operator_names) > 0)
self.assertEqual(len(tracker.memories_allocated), tracker._op_index)
self.assertEqual(len(tracker.memories_active), tracker._op_index)
self.assertEqual(len(tracker.memories_reserved), tracker._op_index)
self.assertTrue(len(tracker._markers) == 2)
self.assertTrue(tracker._cur_module_name != "")
self.assertTrue(hasattr(tracker, "_num_alloc_retries"))
if __name__ == "__main__":
run_tests()
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