forked from mrq/bitsandbytes-rocm
58 lines
3.1 KiB
Python
58 lines
3.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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from bitsandbytes.optim.optimizer import Optimizer1State
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torch.optim.Adagrad
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class Adagrad(Optimizer1State):
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def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
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optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if initial_accumulator_value != 0.0:
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raise ValueError('Initial accumulator value != 0.0 not supported!')
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if lr_decay != 0.0:
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raise ValueError('Lr Decay != 0.0 not supported!')
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super(Adagrad, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
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weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise)
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class Adagrad8bit(Optimizer1State):
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def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
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optim_bits=8, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if initial_accumulator_value != 0.0:
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raise ValueError('Initial accumulator value != 0.0 not supported!')
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if lr_decay != 0.0:
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raise ValueError('Lr Decay != 0.0 not supported!')
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assert block_wise
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super(Adagrad8bit, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
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weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise)
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class Adagrad32bit(Optimizer1State):
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def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
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optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if initial_accumulator_value != 0.0:
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raise ValueError('Initial accumulator value != 0.0 not supported!')
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if lr_decay != 0.0:
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raise ValueError('Lr Decay != 0.0 not supported!')
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super(Adagrad32bit, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
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weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)
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