File size: 3,556 Bytes
09a3fa9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
import torch.nn as nn
import mmengine
from mmengine.device import get_device
from mmengine.runner import autocast
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
class TestAmp(unittest.TestCase):
def test_autocast(self):
if not torch.cuda.is_available():
if digit_version(TORCH_VERSION) < digit_version('1.10.0'):
# `torch.cuda.amp.autocast` is only support in gpu mode, if
# cuda is not available, it will return an empty context and
# should not accept any arguments.
with self.assertRaisesRegex(RuntimeError,
'If pytorch versions is '):
with autocast():
pass
with autocast(enabled=False):
layer = nn.Conv2d(1, 1, 1)
res = layer(torch.randn(1, 1, 1, 1))
self.assertEqual(res.dtype, torch.float32)
else:
with autocast(device_type='cpu'):
# torch.autocast support cpu mode.
layer = nn.Conv2d(1, 1, 1)
res = layer(torch.randn(1, 1, 1, 1))
self.assertIn(res.dtype, (torch.bfloat16, torch.float16))
with autocast(enabled=False):
res = layer(torch.randn(1, 1, 1, 1))
self.assertEqual(res.dtype, torch.float32)
else:
if digit_version(TORCH_VERSION) < digit_version('1.10.0'):
devices = ['cuda']
else:
devices = ['cpu', 'cuda']
for device in devices:
with autocast(device_type=device):
# torch.autocast support cpu and cuda mode.
layer = nn.Conv2d(1, 1, 1).to(device)
res = layer(torch.randn(1, 1, 1, 1).to(device))
self.assertIn(res.dtype, (torch.bfloat16, torch.float16))
with autocast(enabled=False, device_type=device):
res = layer(torch.randn(1, 1, 1, 1).to(device))
self.assertEqual(res.dtype, torch.float32)
# Test with fp32_enabled
with autocast(enabled=False, device_type=device):
layer = nn.Conv2d(1, 1, 1).to(device)
res = layer(torch.randn(1, 1, 1, 1).to(device))
self.assertEqual(res.dtype, torch.float32)
# Test mps
if digit_version(TORCH_VERSION) >= digit_version('1.12.0'):
mmengine.runner.amp.get_device = lambda: 'mps'
with autocast(enabled=False):
layer = nn.Conv2d(1, 1, 1)
res = layer(torch.randn(1, 1, 1, 1))
self.assertEqual(res.dtype, torch.float32)
with self.assertRaisesRegex(ValueError,
'User specified autocast device_type'):
with autocast(enabled=True):
pass
# Native pytorch does not support mlu, here we simply test autocast
# will call `torch.autocast`, which will be overridden by mlu version
# pytorch
mmengine.runner.amp.get_device = lambda: 'mlu'
with self.assertRaises(RuntimeError):
with autocast(enabled=False):
pass
mmengine.runner.amp.get_device = get_device
|