656 lines
22 KiB
Python
656 lines
22 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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import os.path as osp
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import tempfile
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import unittest
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from itertools import product
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from unittest import TestCase
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from unittest.mock import patch
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import torch
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import torch.distributed as torch_dist
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import mmengine.dist as dist
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from mmengine.dist.dist import sync_random_seed
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from mmengine.testing._internal import MultiProcessTestCase
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from mmengine.utils import digit_version
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from mmengine.utils.dl_utils import TORCH_VERSION
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class TestDist(TestCase):
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"""Test dist module in non-distributed environment."""
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def test_all_reduce(self):
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data = torch.arange(2, dtype=torch.int64)
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expected = torch.arange(2, dtype=torch.int64)
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dist.all_reduce(data)
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self.assertTrue(torch.allclose(data, expected))
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def test_all_gather(self):
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data = torch.arange(2, dtype=torch.int64)
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expected = torch.arange(2, dtype=torch.int64)
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output = dist.all_gather(data)
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self.assertTrue(torch.allclose(output[0], expected))
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def test_gather(self):
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data = torch.arange(2, dtype=torch.int64)
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expected = torch.arange(2, dtype=torch.int64)
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output = dist.gather(data)
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self.assertTrue(torch.allclose(output[0], expected))
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def test_broadcast(self):
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data = torch.arange(2, dtype=torch.int64)
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expected = torch.arange(2, dtype=torch.int64)
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dist.broadcast(data)
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self.assertTrue(torch.allclose(data, expected))
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@patch('numpy.random.randint', return_value=10)
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def test_sync_random_seed(self, mock):
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self.assertEqual(sync_random_seed(), 10)
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def test_broadcast_object_list(self):
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with self.assertRaises(AssertionError):
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# input should be list of object
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dist.broadcast_object_list('foo')
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data = ['foo', 12, {1: 2}]
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expected = ['foo', 12, {1: 2}]
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dist.broadcast_object_list(data)
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self.assertEqual(data, expected)
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def test_all_reduce_dict(self):
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with self.assertRaises(AssertionError):
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# input should be dict
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dist.all_reduce_dict('foo')
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data = {
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'key1': torch.arange(2, dtype=torch.int64),
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'key2': torch.arange(3, dtype=torch.int64)
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}
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expected = {
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'key1': torch.arange(2, dtype=torch.int64),
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'key2': torch.arange(3, dtype=torch.int64)
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}
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dist.all_reduce_dict(data)
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for key in data:
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self.assertTrue(torch.allclose(data[key], expected[key]))
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def test_all_gather_object(self):
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data = 'foo'
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expected = 'foo'
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gather_objects = dist.all_gather_object(data)
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self.assertEqual(gather_objects[0], expected)
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def test_gather_object(self):
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data = 'foo'
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expected = 'foo'
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gather_objects = dist.gather_object(data)
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self.assertEqual(gather_objects[0], expected)
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def test_collect_results(self):
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data = ['foo', {1: 2}]
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size = 2
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expected = ['foo', {1: 2}]
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# test `device=cpu`
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output = dist.collect_results(data, size, device='cpu')
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self.assertEqual(output, expected)
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# test `device=gpu`
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output = dist.collect_results(data, size, device='gpu')
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self.assertEqual(output, expected)
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def test_all_reduce_params(self):
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for tensor_type, reduce_op in zip([torch.int64, torch.float32],
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['sum', 'mean']):
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data = [
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torch.tensor([0, 1], dtype=tensor_type) for _ in range(100)
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]
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data_gen = (item for item in data)
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expected = [
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torch.tensor([0, 1], dtype=tensor_type) for _ in range(100)
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]
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dist.all_reduce_params(data_gen, op=reduce_op)
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for item1, item2 in zip(data, expected):
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self.assertTrue(torch.allclose(item1, item2))
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class TestDistWithGLOOBackend(MultiProcessTestCase):
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def _init_dist_env(self, rank, world_size):
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"""Initialize the distributed environment."""
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os.environ['MASTER_ADDR'] = '127.0.0.1'
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os.environ['MASTER_PORT'] = '29505'
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os.environ['RANK'] = str(rank)
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torch_dist.init_process_group(
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backend='gloo', rank=rank, world_size=world_size)
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def setUp(self):
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super().setUp()
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self._spawn_processes()
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def test_all_reduce(self):
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self._init_dist_env(self.rank, self.world_size)
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tensor_types = [torch.int64, torch.float32, torch.int64]
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reduce_ops = ['sum', 'mean', 'mean']
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for tensor_type, reduce_op in zip(tensor_types, reduce_ops):
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if dist.get_rank() == 0:
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data = torch.tensor([1, 2], dtype=tensor_type)
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else:
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data = torch.tensor([3, 4], dtype=tensor_type)
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if reduce_op == 'sum':
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expected = torch.tensor([4, 6], dtype=tensor_type)
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else:
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expected = torch.tensor([2, 3], dtype=tensor_type)
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dist.all_reduce(data, reduce_op)
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self.assertTrue(torch.allclose(data, expected))
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def test_all_gather(self):
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self._init_dist_env(self.rank, self.world_size)
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if dist.get_rank() == 0:
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data = torch.tensor([0, 1])
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else:
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data = torch.tensor([1, 2])
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expected = [torch.tensor([0, 1]), torch.tensor([1, 2])]
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output = dist.all_gather(data)
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self.assertTrue(
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torch.allclose(output[dist.get_rank()], expected[dist.get_rank()]))
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def test_gather(self):
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self._init_dist_env(self.rank, self.world_size)
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if dist.get_rank() == 0:
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data = torch.tensor([0, 1])
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else:
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data = torch.tensor([1, 2])
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output = dist.gather(data)
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if dist.get_rank() == 0:
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expected = [torch.tensor([0, 1]), torch.tensor([1, 2])]
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for i in range(2):
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assert torch.allclose(output[i], expected[i])
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else:
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assert output == []
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def test_broadcast_dist(self):
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self._init_dist_env(self.rank, self.world_size)
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if dist.get_rank() == 0:
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data = torch.tensor([0, 1])
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else:
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data = torch.tensor([1, 2])
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expected = torch.tensor([0, 1])
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dist.broadcast(data, 0)
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assert torch.allclose(data, expected)
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def test_sync_random_seed(self):
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self._init_dist_env(self.rank, self.world_size)
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with patch.object(
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torch, 'tensor',
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return_value=torch.tensor(1024)) as mock_tensor:
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output = dist.sync_random_seed()
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assert output == 1024
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mock_tensor.assert_called()
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def test_broadcast_object_list(self):
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self._init_dist_env(self.rank, self.world_size)
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if dist.get_rank() == 0:
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data = ['foo', 12, {1: 2}]
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else:
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data = [None, None, None]
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expected = ['foo', 12, {1: 2}]
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dist.broadcast_object_list(data)
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self.assertEqual(data, expected)
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def test_all_reduce_dict(self):
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self._init_dist_env(self.rank, self.world_size)
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for tensor_type, reduce_op in zip([torch.int64, torch.float32],
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['sum', 'mean']):
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if dist.get_rank() == 0:
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data = {
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'key1': torch.tensor([0, 1], dtype=tensor_type),
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'key2': torch.tensor([1, 2], dtype=tensor_type),
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}
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else:
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data = {
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'key1': torch.tensor([2, 3], dtype=tensor_type),
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'key2': torch.tensor([3, 4], dtype=tensor_type),
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}
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if reduce_op == 'sum':
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expected = {
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'key1': torch.tensor([2, 4], dtype=tensor_type),
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'key2': torch.tensor([4, 6], dtype=tensor_type),
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}
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else:
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expected = {
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'key1': torch.tensor([1, 2], dtype=tensor_type),
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'key2': torch.tensor([2, 3], dtype=tensor_type),
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}
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dist.all_reduce_dict(data, reduce_op)
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for key in data:
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assert torch.allclose(data[key], expected[key])
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# `torch.cat` in torch1.5 can not concatenate different types so we
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# fallback to convert them all to float type.
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if digit_version(TORCH_VERSION) == digit_version('1.5.0'):
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if dist.get_rank() == 0:
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data = {
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'key1': torch.tensor([0, 1], dtype=torch.float32),
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'key2': torch.tensor([1, 2], dtype=torch.int32)
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}
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else:
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data = {
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'key1': torch.tensor([2, 3], dtype=torch.float32),
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'key2': torch.tensor([3, 4], dtype=torch.int32),
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}
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expected = {
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'key1': torch.tensor([2, 4], dtype=torch.float32),
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'key2': torch.tensor([4, 6], dtype=torch.float32),
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}
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dist.all_reduce_dict(data, 'sum')
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for key in data:
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assert torch.allclose(data[key], expected[key])
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def test_all_gather_object(self):
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self._init_dist_env(self.rank, self.world_size)
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# data is a pickable python object
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if dist.get_rank() == 0:
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data = 'foo'
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else:
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data = {1: 2}
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expected = ['foo', {1: 2}]
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output = dist.all_gather_object(data)
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self.assertEqual(output, expected)
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# data is a list of pickable python object
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if dist.get_rank() == 0:
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data = ['foo', {1: 2}]
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else:
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data = {2: 3}
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expected = [['foo', {1: 2}], {2: 3}]
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output = dist.all_gather_object(data)
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self.assertEqual(output, expected)
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def test_gather_object(self):
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self._init_dist_env(self.rank, self.world_size)
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# data is a pickable python object
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if dist.get_rank() == 0:
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data = 'foo'
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else:
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data = {1: 2}
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output = dist.gather_object(data, dst=0)
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if dist.get_rank() == 0:
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self.assertEqual(output, ['foo', {1: 2}])
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else:
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self.assertIsNone(output)
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# data is a list of pickable python object
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if dist.get_rank() == 0:
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data = ['foo', {1: 2}]
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else:
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data = {2: 3}
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output = dist.gather_object(data, dst=0)
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if dist.get_rank() == 0:
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self.assertEqual(output, [['foo', {1: 2}], {2: 3}])
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else:
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self.assertIsNone(output)
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def test_all_reduce_params(self):
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self._init_dist_env(self.rank, self.world_size)
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tensor_types = [torch.int64, torch.float32]
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reduce_ops = ['sum', 'mean']
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coalesces = [True, False]
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for tensor_type, reduce_op, coalesce in zip(tensor_types, reduce_ops,
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coalesces):
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if dist.get_rank() == 0:
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data = [
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torch.tensor([0, 1], dtype=tensor_type) for _ in range(100)
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]
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else:
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data = (
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torch.tensor([2, 3], dtype=tensor_type)
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for _ in range(100))
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data_gen = (item for item in data)
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if reduce_op == 'sum':
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expected = (
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torch.tensor([2, 4], dtype=tensor_type)
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for _ in range(100))
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else:
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expected = (
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torch.tensor([1, 2], dtype=tensor_type)
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for _ in range(100))
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dist.all_reduce_params(data_gen, coalesce=coalesce, op=reduce_op)
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for item1, item2 in zip(data, expected):
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self.assertTrue(torch.allclose(item1, item2))
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@unittest.skipIf(
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torch.cuda.device_count() < 2, reason='need 2 gpu to test nccl')
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class TestDistWithNCCLBackend(MultiProcessTestCase):
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def _init_dist_env(self, rank, world_size):
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"""Initialize the distributed environment."""
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os.environ['MASTER_ADDR'] = '127.0.0.1'
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os.environ['MASTER_PORT'] = '29505'
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os.environ['RANK'] = str(rank)
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num_gpus = torch.cuda.device_count()
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torch.cuda.set_device(rank % num_gpus)
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torch_dist.init_process_group(
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backend='nccl', rank=rank, world_size=world_size)
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def setUp(self):
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super().setUp()
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self._spawn_processes()
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def test_all_reduce(self):
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self._init_dist_env(self.rank, self.world_size)
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tensor_types = [torch.int64, torch.float32]
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reduce_ops = ['sum', 'mean']
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device_types = ['cpu', 'cuda']
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for tensor_type, reduce_op, device_type in product(
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tensor_types, reduce_ops, device_types):
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# 'mean' op does not support torch.int64
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if tensor_type == torch.int64 and reduce_op == 'mean':
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continue
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if dist.get_rank() == 0:
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data = torch.tensor([1, 2], dtype=tensor_type).to(device_type)
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else:
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data = torch.tensor([3, 4], dtype=tensor_type).to(device_type)
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if reduce_op == 'sum':
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expected = torch.tensor([4, 6],
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dtype=tensor_type).to(device_type)
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else:
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expected = torch.tensor([2, 3],
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dtype=tensor_type).to(device_type)
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dist.all_reduce(data, reduce_op)
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self.assertTrue(torch.allclose(data, expected))
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def test_all_gather(self):
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self._init_dist_env(self.rank, self.world_size)
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for device_type in ('cpu', 'cuda'):
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if dist.get_rank() == 0:
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data = torch.tensor([0, 1]).to(device_type)
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else:
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data = torch.tensor([1, 2]).to(device_type)
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expected = [
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torch.tensor([0, 1]).to(device_type),
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torch.tensor([1, 2]).to(device_type)
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]
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output = dist.all_gather(data)
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self.assertTrue(
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torch.allclose(output[dist.get_rank()],
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expected[dist.get_rank()]))
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def test_broadcast_dist(self):
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self._init_dist_env(self.rank, self.world_size)
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for device_type in ('cpu', 'cuda'):
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if dist.get_rank() == 0:
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data = torch.tensor([0, 1]).to(device_type)
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else:
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data = torch.tensor([1, 2]).to(device_type)
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expected = torch.tensor([0, 1]).to(device_type)
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dist.broadcast(data, 0)
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assert torch.allclose(data, expected)
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def test_sync_random_seed(self):
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self._init_dist_env(self.rank, self.world_size)
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with patch.object(
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torch, 'tensor',
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return_value=torch.tensor(1024)) as mock_tensor:
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output = dist.sync_random_seed()
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assert output == 1024
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mock_tensor.assert_called()
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def test_broadcast_object_list(self):
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self._init_dist_env(self.rank, self.world_size)
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if dist.get_rank() == 0:
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data = ['foo', 12, {1: 2}]
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else:
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data = [None, None, None]
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expected = ['foo', 12, {1: 2}]
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dist.broadcast_object_list(data)
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self.assertEqual(data, expected)
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def test_all_reduce_dict(self):
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self._init_dist_env(self.rank, self.world_size)
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tensor_types = [torch.int64, torch.float32]
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reduce_ops = ['sum', 'mean']
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device_types = ['cpu', 'cuda']
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for tensor_type, reduce_op, device_type in product(
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tensor_types, reduce_ops, device_types):
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# 'mean' op does not support torch.int64
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if tensor_type == torch.int64 and reduce_op == 'mean':
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continue
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if dist.get_rank() == 0:
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data = {
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'key1':
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torch.tensor([0, 1], dtype=tensor_type).to(device_type),
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'key2':
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torch.tensor([1, 2], dtype=tensor_type).to(device_type),
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}
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else:
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data = {
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'key1':
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torch.tensor([2, 3], dtype=tensor_type).to(device_type),
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'key2':
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torch.tensor([3, 4], dtype=tensor_type).to(device_type),
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}
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if reduce_op == 'sum':
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expected = {
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'key1':
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torch.tensor([2, 4], dtype=tensor_type).to(device_type),
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'key2':
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torch.tensor([4, 6], dtype=tensor_type).to(device_type),
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}
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else:
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expected = {
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'key1':
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torch.tensor([1, 2], dtype=tensor_type).to(device_type),
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'key2':
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torch.tensor([2, 3], dtype=tensor_type).to(device_type),
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}
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dist.all_reduce_dict(data, reduce_op)
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for key in data:
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assert torch.allclose(data[key], expected[key])
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# `torch.cat` in torch1.5 can not concatenate different types so we
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# fallback to convert them all to float type.
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for device_type in ('cpu', 'cuda'):
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if digit_version(TORCH_VERSION) == digit_version('1.5.0'):
|
|
if dist.get_rank() == 0:
|
|
data = {
|
|
'key1':
|
|
torch.tensor([0, 1],
|
|
dtype=torch.float32).to(device_type),
|
|
'key2':
|
|
torch.tensor([1, 2],
|
|
dtype=torch.int32).to(device_type),
|
|
}
|
|
else:
|
|
data = {
|
|
'key1':
|
|
torch.tensor([2, 3],
|
|
dtype=torch.float32).to(device_type),
|
|
'key2':
|
|
torch.tensor([3, 4],
|
|
dtype=torch.int32).to(device_type),
|
|
}
|
|
|
|
expected = {
|
|
'key1':
|
|
torch.tensor([2, 4], dtype=torch.float32).to(device_type),
|
|
'key2':
|
|
torch.tensor([4, 6], dtype=torch.float32).to(device_type),
|
|
}
|
|
|
|
dist.all_reduce_dict(data, 'sum')
|
|
|
|
for key in data:
|
|
assert torch.allclose(data[key], expected[key])
|
|
|
|
def test_all_gather_object(self):
|
|
self._init_dist_env(self.rank, self.world_size)
|
|
|
|
# data is a pickable python object
|
|
if dist.get_rank() == 0:
|
|
data = 'foo'
|
|
else:
|
|
data = {1: 2}
|
|
|
|
expected = ['foo', {1: 2}]
|
|
output = dist.all_gather_object(data)
|
|
|
|
self.assertEqual(output, expected)
|
|
|
|
# data is a list of pickable python object
|
|
if dist.get_rank() == 0:
|
|
data = ['foo', {1: 2}]
|
|
else:
|
|
data = {2: 3}
|
|
|
|
expected = [['foo', {1: 2}], {2: 3}]
|
|
output = dist.all_gather_object(data)
|
|
|
|
self.assertEqual(output, expected)
|
|
|
|
def test_collect_results(self):
|
|
self._init_dist_env(self.rank, self.world_size)
|
|
|
|
# 1. test `device` and `tmpdir` parameters
|
|
if dist.get_rank() == 0:
|
|
data = ['foo', {1: 2}]
|
|
else:
|
|
data = [24, {'a': 'b'}]
|
|
|
|
size = 4
|
|
|
|
expected = ['foo', 24, {1: 2}, {'a': 'b'}]
|
|
|
|
# 1.1 test `device=cpu` and `tmpdir` is None
|
|
output = dist.collect_results(data, size, device='cpu')
|
|
if dist.get_rank() == 0:
|
|
self.assertEqual(output, expected)
|
|
else:
|
|
self.assertIsNone(output)
|
|
|
|
# 1.2 test `device=cpu` and `tmpdir` is not None
|
|
tmpdir = tempfile.mkdtemp()
|
|
# broadcast tmpdir to all ranks to make it consistent
|
|
object_list = [tmpdir]
|
|
dist.broadcast_object_list(object_list)
|
|
output = dist.collect_results(
|
|
data, size, device='cpu', tmpdir=object_list[0])
|
|
if dist.get_rank() == 0:
|
|
self.assertEqual(output, expected)
|
|
else:
|
|
self.assertIsNone(output)
|
|
|
|
if dist.get_rank() == 0:
|
|
# object_list[0] will be removed by `dist.collect_results`
|
|
self.assertFalse(osp.exists(object_list[0]))
|
|
|
|
# 1.3 test `device=gpu`
|
|
output = dist.collect_results(data, size, device='gpu')
|
|
if dist.get_rank() == 0:
|
|
self.assertEqual(output, expected)
|
|
else:
|
|
self.assertIsNone(output)
|
|
|
|
# 2. test `size` parameter
|
|
if dist.get_rank() == 0:
|
|
data = ['foo', {1: 2}]
|
|
else:
|
|
data = [24, {'a': 'b'}]
|
|
|
|
size = 3
|
|
|
|
expected = ['foo', 24, {1: 2}]
|
|
|
|
# 2.1 test `device=cpu` and `tmpdir` is None
|
|
output = dist.collect_results(data, size, device='cpu')
|
|
if dist.get_rank() == 0:
|
|
self.assertEqual(output, expected)
|
|
else:
|
|
self.assertIsNone(output)
|
|
|
|
# 2.2 test `device=gpu`
|
|
output = dist.collect_results(data, size, device='gpu')
|
|
if dist.get_rank() == 0:
|
|
self.assertEqual(output, expected)
|
|
else:
|
|
self.assertIsNone(output)
|
|
|
|
def test_all_reduce_params(self):
|
|
self._init_dist_env(self.rank, self.world_size)
|
|
|
|
tensor_types = [torch.int64, torch.float32]
|
|
reduce_ops = ['sum', 'mean']
|
|
coalesces = [True, False]
|
|
device_types = ['cpu', 'cuda']
|
|
for tensor_type, reduce_op, coalesce, device_type in zip(
|
|
tensor_types, reduce_ops, coalesces, device_types):
|
|
if dist.get_rank() == 0:
|
|
data = [
|
|
torch.tensor([0, 1], dtype=tensor_type).to(device_type)
|
|
for _ in range(100)
|
|
]
|
|
else:
|
|
data = [
|
|
torch.tensor([2, 3], dtype=tensor_type).to(device_type)
|
|
for _ in range(100)
|
|
]
|
|
|
|
data_gen = (item for item in data)
|
|
|
|
if reduce_op == 'sum':
|
|
expected = (
|
|
torch.tensor([2, 4], dtype=tensor_type).to(device_type)
|
|
for _ in range(100))
|
|
else:
|
|
expected = (
|
|
torch.tensor([1, 2], dtype=tensor_type).to(device_type)
|
|
for _ in range(100))
|
|
|
|
for item1, item2 in zip(data_gen, expected):
|
|
self.assertTrue(torch.allclose(item1, item2))
|