46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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import torch
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class Fp16Optimizer:
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def __init__(self, fp16_model, loss_scale=8192.0):
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print('Initializing fp16 optimizer')
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self.initialize_model(fp16_model)
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self.loss_scale = loss_scale
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def initialize_model(self, model):
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print('Reset fp16 grad')
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self.fp16_model = model
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for param in self.fp16_model.parameters():
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param.grad = None
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print('Initializing fp32 clone weights')
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self.fp32_params = [param.clone().type(torch.cuda.FloatTensor).detach()
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for param in model.parameters()]
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for param in self.fp32_params:
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param.requires_grad = True
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def backward(self, loss):
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loss *= self.loss_scale
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loss.backward()
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def step(self, optimizer):
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optimizer.step(grads=[p.grad for p in self.fp16_model.parameters()],
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output_params=self.fp16_model.parameters(), scale=self.loss_scale)
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for p in self.fp16_model.parameters():
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p.grad = None
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