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db3290846e
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129761 Approved by: https://github.com/fegin
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
# Owner(s): ["oncall: distributed"]
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import os
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import unittest
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import torch
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import torch.nn as nn
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from torch.distributed._tools import MemoryTracker
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from torch.testing._internal.common_cuda import TEST_CUDA
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from torch.testing._internal.common_utils import run_tests, TestCase
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class TestMemoryTracker(TestCase):
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@unittest.skipIf(not TEST_CUDA, "no cuda")
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def test_local_model(self):
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"""
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Minimal test case to check the memory tracker can collect the expected
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memory stats at operator level, as well as can print the summary result
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without crash.
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"""
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# Create a model with a hierarchy of modules
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torch.manual_seed(0)
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model = nn.Sequential(
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nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=(3, 3), padding=(1, 1), bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=False),
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nn.AdaptiveAvgPool2d(output_size=(1, 1)),
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),
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nn.Flatten(start_dim=1),
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nn.Sequential(nn.Linear(64, 2), nn.ReLU(inplace=True)),
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).cuda()
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# Run one iteration of forward and backward pass
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tracker = MemoryTracker()
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tracker.start_monitor(model)
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x = torch.randn(size=(2, 3, 224, 224), device=torch.device("cuda"))
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# torch.LongTensor expects cpu device type, not cuda device type in
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# constructor, so calling .cuda() outside constructor here.
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target = torch.LongTensor([0, 1]).cuda()
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criterion = nn.CrossEntropyLoss()
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criterion(model(x), target).backward()
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self.assertTrue(len(tracker._hooks) > 0)
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tracker.stop()
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self.assertTrue(len(tracker._hooks) == 0)
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path = "memory.trace"
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tracker.save_stats(path)
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tracker.load(path)
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tracker.summary()
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if os.path.exists(path):
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os.remove(path)
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self.assertTrue(tracker._op_index > 0)
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self.assertTrue(len(tracker._operator_names) > 0)
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self.assertEqual(len(tracker.memories_allocated), tracker._op_index)
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self.assertEqual(len(tracker.memories_active), tracker._op_index)
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self.assertEqual(len(tracker.memories_reserved), tracker._op_index)
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self.assertTrue(len(tracker._markers) == 2)
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self.assertTrue(tracker._cur_module_name != "")
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self.assertTrue(hasattr(tracker, "_num_cuda_retries"))
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if __name__ == "__main__":
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run_tests()
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