Spaces:
Build error
Build error
File size: 7,123 Bytes
b6af722 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import numpy as np
from cosmos_predict1.utils import distributed, log
class TeroPolyScheduler:
def __init__(
self,
total_Mimg: int,
batch_size: int,
ref_Mimg: Optional[int] = None,
ref_batches: float = 70e3 / 1024,
max_lr_ratio: Optional[float] = 1.0,
min_lr_ratio: Optional[float] = None,
rampup_Mimg: float = 0,
rampdown_Mimg: int = 0,
verbosity_interval: int = 0,
formula: str = "poly",
poly_exp: float = 0.5,
):
self.total_Mimg = total_Mimg
self.batch_size = batch_size * distributed.get_world_size()
self.ref_Mimg = ref_Mimg or ref_batches * batch_size / 1e6
self.ref_batches = ref_batches
self.max_lr_ratio = max_lr_ratio
self.min_lr_ratio = min_lr_ratio
self.rampup_Mimg = rampup_Mimg
self.rampdown_Mimg = rampdown_Mimg
self.verbosity_interval = verbosity_interval
self.formula = formula
self.poly_exp = poly_exp
self._model = None
@property
def model(self):
return self._model
@model.setter
def model(self, model):
self._model = model
def schedule(self, n, **kwargs):
cur_Mimg = getattr(self.model, "sample_counter", 0) / 1e6
if self.formula == "constant":
lr = 1.0
elif self.formula == "poly":
lr = max(cur_Mimg / self.ref_Mimg, 1e-8) ** -self.poly_exp
else:
raise ValueError(f'Invalid learning rate formula "{self.formula}"')
if self.max_lr_ratio is not None:
lr = min(lr, self.max_lr_ratio)
if self.min_lr_ratio is not None:
lr = max(lr, self.min_lr_ratio)
if self.rampup_Mimg > 0 and cur_Mimg < self.rampup_Mimg:
lr *= cur_Mimg / self.rampup_Mimg
if self.rampdown_Mimg > 0 and cur_Mimg > self.total_Mimg - self.rampdown_Mimg:
lr *= (self.total_Mimg - cur_Mimg) / self.rampdown_Mimg
return lr
def __call__(self, n, **kwargs):
return self.schedule(n, **kwargs)
class LambdaWarmUpCosineScheduler:
"""
A learning rate scheduler that combines warm-up with a cosine decay schedule for multiple cycles.
It supports different configurations for each cycle, including the number of warm-up steps, minimum
and maximum scaling factors for the learning rate.
The scheduler is intended to be used with a base learning rate of 1.0, where the actual learning
rate at any step is the base learning rate multiplied by the scaling factor computed by the scheduler.
Parameters:
warm_up_steps (list[int]): List of integers where each element represents the number of warm-up
steps for the corresponding cycle.
f_min (list[float]): List of the minimum scaling factors for each cycle after warm-up.
f_max (list[float]): List of the maximum scaling factors at the start and end of each cosine cycle.
f_start (list[float]): List of starting scaling factors for each warm-up phase.
cycle_lengths (list[int]): List of the total lengths of each cycle, including warm-up steps.
verbosity_interval (int, optional): Interval of training steps at which to print current step and
scaling factor information. Set to 0 by default to disable verbosity.
Examples:
>>> scheduler = LambdaWarmUpCosineScheduler2(
warm_up_steps=[10, 10],
f_min=[0.1, 0.1],
f_max=[1.0, 1.0],
f_start=[0.01, 0.01],
cycle_lengths=[50, 50],
verbosity_interval=10)
>>> for step in range(100):
>>> lr_multiplier = scheduler(step)
>>> print(f"Step {step}: LR Multiplier = {lr_multiplier}")
"""
def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
self.lr_warm_up_steps = warm_up_steps
self.f_start = f_start
self.f_min = f_min
self.f_max = f_max
self.cycle_lengths = cycle_lengths
self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
self.last_f = 0.0
self.verbosity_interval = verbosity_interval
def find_in_interval(self, n):
interval = 0
for cl in self.cum_cycles[1:]:
if n <= cl:
return interval
interval += 1
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0:
log.info(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}")
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
t = min(t, 1.0)
f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (1 + np.cos(t * np.pi))
self.last_f = f
return f
def __call__(self, n, **kwargs):
return self.schedule(n, **kwargs)
class LambdaLinearScheduler(LambdaWarmUpCosineScheduler):
"""
Linear instead of cosine decay for the main part of the cycle.
"""
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0:
log.info(f"current step: {n}, recent lr-multiplier: {self.last_f}, " f"current cycle {cycle}")
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (
self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
)
self.last_f = f
return f
|