mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
d25e6e623f
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134665 Approved by: https://github.com/albanD
131 lines
3.8 KiB
Python
131 lines
3.8 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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from collections import OrderedDict
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from dataclasses import dataclass
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from typing import List
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import torch
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import torch.nn as nn
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from torch import distributed as dist
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from torch.distributed.utils import _apply_to_tensors, _replace_by_prefix
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from torch.testing._internal.common_utils import (
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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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TEST_WITH_DEV_DBG_ASAN,
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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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@dataclass
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class NonFrozenDataClass:
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some_key: str
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some_float: float
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some_tensor: List[torch.Tensor]
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@dataclass(frozen=True)
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class FrozenDataClass:
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some_key: str
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some_float: float
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some_tensor: List[torch.Tensor]
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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, {"x", get_a_tensor(), get_a_tensor()})
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data.append(([1], get_a_tensor(), (1), [get_a_tensor()], {1, 2}))
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data.append(
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{"non_frozen_ds": NonFrozenDataClass("some_key", 1.0, [get_a_tensor()])}
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)
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data.append({"frozen_ds": FrozenDataClass("some_key", 1.0, [get_a_tensor()])})
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od = OrderedDict()
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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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def test_replace_by_prefix(self):
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state_dict = {
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"layer.a": torch.tensor(1),
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"abc.layer.def": torch.tensor(2),
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"layer.b": torch.tensor(3),
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}
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original_state_dict = state_dict.copy()
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_replace_by_prefix(state_dict, "layer.", "module.layer.")
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assert state_dict == {
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"module.layer.a": torch.tensor(1),
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"abc.layer.def": torch.tensor(2),
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"module.layer.b": torch.tensor(3),
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}
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_replace_by_prefix(state_dict, "module.layer.", "layer.")
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assert state_dict == original_state_dict
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def test_packed_sequence(self):
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"""Test to ensure RNN packed sequences are modified correctly."""
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rnn = nn.RNN(5, 5)
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x = torch.rand((5, 1, 5), dtype=torch.float)
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seq_length = torch.tensor([4], dtype=torch.int)
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def fill_fn(x):
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x.fill_(0)
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x = nn.utils.rnn.pack_padded_sequence(x, seq_length)
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x, _ = rnn(x)
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x = _apply_to_tensors(fill_fn, x)
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x, _ = nn.utils.rnn.pad_packed_sequence(x)
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self.assertEqual(torch.sum(x), 0)
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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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