82 lines
3.3 KiB
Python
82 lines
3.3 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pytorch_lightning import _logger as log
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from pytorch_lightning.plugins.apex import ApexPlugin
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from pytorch_lightning.plugins.native_amp import NativeAMP
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from pytorch_lightning.utilities import APEX_AVAILABLE, NATIVE_AMP_AVALAIBLE, AMPType, rank_zero_warn
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class PrecisionConnector:
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def __init__(self, trainer):
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self.trainer = trainer
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self.backend = None
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def on_trainer_init(self, precision, amp_level, amp_backend):
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# AMP init
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# These are the only lines needed after v0.8.0
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# we wrap the user's forward with autocast and give it back at the end of fit
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self.trainer.autocast_original_forward = None
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self.trainer.precision = precision
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self.trainer.scaler = None
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self.trainer.amp_level = amp_level
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self.init_amp(amp_backend)
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def init_amp(self, amp_type: str):
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assert self.trainer.precision in (16, 32), 'only 32 or 16 bit precision supported'
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self.trainer.amp_backend = None
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self._setup_amp_backend(amp_type)
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def _setup_amp_backend(self, amp_type: str):
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if self.trainer.precision != 16:
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# no AMP requested, so we can leave now
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return
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amp_type = amp_type.lower()
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assert amp_type in ('native', 'apex'), f'Unsupported amp type {amp_type}'
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if amp_type == 'native':
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if not NATIVE_AMP_AVALAIBLE:
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rank_zero_warn('You have asked for native AMP but your PyTorch version does not support it.'
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' Consider upgrading with `pip install torch>=1.6`.'
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' We will attempt to use NVIDIA Apex for this session.')
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amp_type = 'apex'
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else:
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log.info('Using native 16bit precision.')
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self.trainer.amp_backend = AMPType.NATIVE
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self.backend = NativeAMP(self.trainer)
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if amp_type == 'apex':
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if not APEX_AVAILABLE:
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rank_zero_warn('You have asked for Apex AMP but you have not installed it yet.'
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' Install apex first using this guide: https://github.com/NVIDIA/apex#linux')
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else:
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log.info('Using APEX 16bit precision.')
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self.trainer.amp_backend = AMPType.APEX
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self.backend = ApexPlugin(self.trainer)
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if not self.trainer.amp_backend:
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raise ModuleNotFoundError(
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f'You have asked for AMP support {amp_type}, but there is no support on your side yet.'
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f' Consider installing torch >= 1.6 or NVIDIA Apex.'
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)
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def connect(self, model):
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if self.backend:
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model, optimizers = self.backend.connect(model, self.trainer.optimizers)
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self.trainer.optimizers = optimizers
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return model
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