mirror of
https://github.com/open-mmlab/mmengine.git
synced 2025-06-03 21:54:44 +08:00
7e1d7af2d9
* Rename data to structure * adjust the way to import module * adjust the way to import module * rename Structure to Data Structures in docs api * rename structure to structures * support using some modules of mmengine without torch * fix circleci config * fix circleci config * fix registry ut * minor fix * move init method from model/utils to model/weight_init.py * move init method from model/utils to model/weight_init.py * move sync_bn to model * move functions depending on torch to dl_utils * format import * fix logging ut * add weight init in model/__init__.py * move get_config and get_model to mmengine/hub * move log_processor.py to mmengine/runner * fix ut * Add TimeCounter in dl_utils/__init__.py
59 lines
2.5 KiB
Python
59 lines
2.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import unittest
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
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)
|