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[Fix] Make autocast compatible with mps (#587)
* [Fix] Make autocast compatible with mps * Enhance unit test * fix unit test * clean the code * fix unit test
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@ -121,9 +121,18 @@ def autocast(device_type: Optional[str] = None,
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assert dtype == torch.bfloat16, (
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'In CPU autocast, only support `torch.bfloat16` dtype')
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elif device_type == 'mlu':
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pass
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else:
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raise ValueError('User specified autocast device_type must be '
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F'cuda or cpu, but got {device_type}')
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# Device like MPS does not support fp16 training or testing.
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# If an inappropriate device is set and fp16 is enabled, an error
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# will be thrown.
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if enabled is False:
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yield
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return
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else:
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raise ValueError('User specified autocast device_type must be '
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f'cuda or cpu, but got {device_type}')
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with torch.autocast(
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device_type=device_type,
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@ -4,6 +4,8 @@ import unittest
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import torch
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import torch.nn as nn
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import mmengine
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from mmengine.device import get_device
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from mmengine.runner import autocast
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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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@ -56,3 +58,24 @@ class TestAmp(unittest.TestCase):
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layer = nn.Conv2d(1, 1, 1).to(device)
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res = layer(torch.randn(1, 1, 1, 1).to(device))
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self.assertEqual(res.dtype, torch.float32)
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# Test mps
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if digit_version(TORCH_VERSION) >= digit_version('1.12.0'):
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mmengine.runner.amp.get_device = lambda: 'mps'
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with autocast(enabled=False):
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layer = nn.Conv2d(1, 1, 1)
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res = layer(torch.randn(1, 1, 1, 1))
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self.assertEqual(res.dtype, torch.float32)
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with self.assertRaisesRegex(ValueError,
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'User specified autocast device_type'):
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with autocast(enabled=True):
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pass
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# Native pytorch does not support mlu, here we simply test autocast
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# will call `torch.autocast`, which will be overridden by mlu version
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# pytorch
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mmengine.runner.amp.get_device = lambda: 'mlu'
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with self.assertRaises(RuntimeError):
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with autocast(enabled=False):
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pass
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mmengine.runner.amp.get_device = get_device
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