0b078403ee
via pyupgrade --py37-plus. The changes e.g. are subclassing from object, calling super() with super(ThisClass, self), or old-style syntax formatting.
133 lines
3.6 KiB
Python
133 lines
3.6 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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from bitsandbytes.optim.optimizer import Optimizer1State
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class Adagrad(Optimizer1State):
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def __init__(
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self,
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params,
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lr=1e-2,
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lr_decay=0,
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weight_decay=0,
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initial_accumulator_value=0,
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eps=1e-10,
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optim_bits=32,
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args=None,
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min_8bit_size=4096,
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percentile_clipping=100,
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block_wise=True,
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):
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if not 0.0 <= lr:
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raise ValueError(f"Invalid learning rate: {lr}")
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if not 0.0 <= weight_decay:
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raise ValueError(
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f"Invalid weight_decay value: {weight_decay}"
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)
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if not 0.0 <= eps:
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raise ValueError(f"Invalid epsilon value: {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().__init__(
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"adagrad",
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params,
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lr,
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(0.0, 0.0),
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eps,
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weight_decay,
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optim_bits,
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args,
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min_8bit_size,
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percentile_clipping,
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block_wise,
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)
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class Adagrad8bit(Optimizer1State):
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def __init__(
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self,
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params,
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lr=1e-2,
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lr_decay=0,
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weight_decay=0,
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initial_accumulator_value=0,
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eps=1e-10,
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optim_bits=8,
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args=None,
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min_8bit_size=4096,
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percentile_clipping=100,
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block_wise=True,
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):
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if not 0.0 <= lr:
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raise ValueError(f"Invalid learning rate: {lr}")
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if not 0.0 <= weight_decay:
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raise ValueError(
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f"Invalid weight_decay value: {weight_decay}"
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)
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if not 0.0 <= eps:
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raise ValueError(f"Invalid epsilon value: {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().__init__(
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"adagrad",
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params,
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lr,
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(0.0, 0.0),
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eps,
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weight_decay,
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8,
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args,
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min_8bit_size,
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percentile_clipping,
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block_wise,
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)
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class Adagrad32bit(Optimizer1State):
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def __init__(
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self,
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params,
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lr=1e-2,
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lr_decay=0,
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weight_decay=0,
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initial_accumulator_value=0,
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eps=1e-10,
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optim_bits=32,
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args=None,
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min_8bit_size=4096,
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percentile_clipping=100,
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block_wise=True,
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):
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if not 0.0 <= lr:
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raise ValueError(f"Invalid learning rate: {lr}")
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if not 0.0 <= weight_decay:
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raise ValueError(
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f"Invalid weight_decay value: {weight_decay}"
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)
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if not 0.0 <= eps:
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raise ValueError(f"Invalid epsilon value: {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().__init__(
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"adagrad",
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params,
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lr,
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(0.0, 0.0),
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eps,
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weight_decay,
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32,
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args,
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min_8bit_size,
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percentile_clipping,
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block_wise,
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)
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