mirror of
https://github.com/zebrajr/pytorch.git
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2336571cb7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72084
make fsdp folder to be public
ghstack-source-id: 148173447
Test Plan: unit tests
Reviewed By: mrshenli
Differential Revision: D33903417
fbshipit-source-id: 7852a2adc4af09af48a5ffa52ebf210489f834d5
(cherry picked from commit bd06513cfe)
81 lines
2.1 KiB
Python
81 lines
2.1 KiB
Python
# Owner(s): ["oncall: distributed"]
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import random
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import sys
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import unittest
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import torch
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from torch import distributed as dist
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from torch.distributed.fsdp.utils import (
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_apply_to_tensors,
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)
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from torch.testing._internal.common_utils import (
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TEST_WITH_DEV_DBG_ASAN,
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instantiate_parametrized_tests,
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parametrize,
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run_tests,
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subtest,
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TestCase,
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)
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if not dist.is_available():
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print("Distributed not available, skipping tests", file=sys.stderr)
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sys.exit(0)
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if TEST_WITH_DEV_DBG_ASAN:
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print(
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"Skip dev-asan as torch + multiprocessing spawn have known issues",
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file=sys.stderr,
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)
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sys.exit(0)
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class TestUtils(TestCase):
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@parametrize(
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"devices", [["cpu"], ["cuda"], subtest(["cpu", "cuda"], name="cpu_cuda")]
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)
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def test_apply_to_tensors(self, devices):
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if "cuda" in devices and (
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not torch.cuda.is_available() or torch.cuda.device_count() < 1
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):
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raise unittest.SkipTest("Skipped due to lack of GPU")
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expected = 0
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def get_a_tensor():
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"""Return a random tensor on random device."""
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dev = random.choice(devices)
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shape = random.choice(((1), (2, 3), (4, 5, 6), (7, 8, 9, 10)))
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t = torch.rand(shape).to(dev)
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nonlocal expected
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expected += t.numel()
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return t
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# create a mixed bag of data.
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data = [1, "str"]
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data.append({"key1": get_a_tensor(), "key2": {1: get_a_tensor()}, "key3": 3})
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data.insert(0, set(["x", get_a_tensor(), get_a_tensor()]))
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data.append(([1], get_a_tensor(), (1), [get_a_tensor()], set((1, 2))))
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od = dict()
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od["k"] = "value"
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data.append(od)
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total = 0
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def fn(t):
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nonlocal total
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total += t.numel()
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return t
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new_data = _apply_to_tensors(fn, data)
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self.assertEqual(total, expected)
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for i, v in enumerate(data):
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self.assertEqual(type(new_data[i]), type(v))
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instantiate_parametrized_tests(TestUtils)
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if __name__ == "__main__":
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run_tests()
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