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# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
"""Megatron number of micro-batches calculators."""
from abc import ABC
from abc import abstractmethod
def build_num_microbatches_calculator(args):
# Constant num micro-batches.
if args.rampup_batch_size is None:
num_microbatches_calculator = ConstantNumMicroBatches(
args.global_batch_size, args.micro_batch_size,
args.data_parallel_size)
if args.rank == 0:
print('setting number of micro-batches to constant {}'.format(
num_microbatches_calculator.get()), flush=True)
else:
assert len(args.rampup_batch_size) == 3, 'expected the following ' \
'format: --rampup-batch-size <start batch size> ' \
'<batch size incerement> <ramp-up samples>'
start_batch_size = int(args.rampup_batch_size[0])
batch_size_increment = int(args.rampup_batch_size[1])
ramup_samples = int(args.rampup_batch_size[2])
if args.rank == 0:
print('will use batch size rampup starting from global batch '
'size {} to global batch size {} with batch size increments '
'{} over {} samples.'.format(start_batch_size,
args.global_batch_size,
batch_size_increment,
ramup_samples), flush=True)
num_microbatches_calculator = RampupBatchsizeNumMicroBatches(
start_batch_size, batch_size_increment, ramup_samples,
args.global_batch_size, args.micro_batch_size,
args.data_parallel_size)
return num_microbatches_calculator
class NumMicroBatchesCalculator(ABC):
def __init__(self):
self.num_micro_batches = None
self.current_global_batch_size = None
def get(self):
return self.num_micro_batches
def get_current_global_batch_size(self):
return self.current_global_batch_size
@abstractmethod
def update(self, consumed_samples, consistency_check):
pass
class ConstantNumMicroBatches(NumMicroBatchesCalculator):
def __init__(self, global_batch_size, micro_batch_size, data_parallel_size):
micro_batch_times_data_parallel = micro_batch_size * \
data_parallel_size
assert global_batch_size % micro_batch_times_data_parallel == 0, \
'global batch size ({}) is not divisible by micro batch size ({})' \
' times data parallel size ({})'.format(global_batch_size,
micro_batch_size,
data_parallel_size)
self.num_micro_batches = global_batch_size // \
micro_batch_times_data_parallel
assert self.num_micro_batches >= 1
self.current_global_batch_size = global_batch_size
def update(self, consumed_samples, consistency_check):
pass
class RampupBatchsizeNumMicroBatches(NumMicroBatchesCalculator):
def __init__(self, start_batch_size, batch_size_increment, ramup_samples,
global_batch_size, micro_batch_size, data_parallel_size):
"""Batch size ramp up.
Over
steps = (global-batch-size - start-batch-size) / batch_size_increment
increment batch size from start-batch-size to global-batch-size using
rampup-samples / steps
samples.
Arguments:
start_batch_size: global batch size to start with
batch_size_increment: global batch size increments
ramup_samples: number of samples to use ramp up global
batch size from `start_batch_size` to `global_batch_size`
global_batch_size: global batch size post rampup
micro_batch_size: micro batch size
data_parallel_size: data parallel size.
"""
self.micro_batch_size = micro_batch_size
self.data_parallel_size = data_parallel_size
self.micro_batch_times_data_parallel_size = self.micro_batch_size * \
self.data_parallel_size
assert self.micro_batch_times_data_parallel_size > 0
assert start_batch_size > 0
self.start_batch_size = start_batch_size
assert global_batch_size > 0
self.global_batch_size = global_batch_size
diff_batch_size = self.global_batch_size - self.start_batch_size
assert diff_batch_size >= 0
assert batch_size_increment > 0
self.batch_size_increment = batch_size_increment
assert diff_batch_size % batch_size_increment == 0, 'expected ' \
'global batch size interval ({}) to be divisible by global batch ' \
'size increment ({})'.format(diff_batch_size, batch_size_increment)
num_increments = diff_batch_size // self.batch_size_increment
self.ramup_samples = ramup_samples
assert self.ramup_samples >= 0
self.rampup_samples_per_increment = self.ramup_samples / num_increments
# Initialize number of microbatches.
self.update(0, False)
def update(self, consumed_samples, consistency_check):
if consumed_samples > self.ramup_samples:
self.current_global_batch_size = self.global_batch_size
else:
steps = int(consumed_samples / self.rampup_samples_per_increment)
self.current_global_batch_size = self.start_batch_size + \
steps * self.batch_size_increment
assert self.current_global_batch_size <= self.global_batch_size
if consistency_check:
assert self.current_global_batch_size % \
self.micro_batch_times_data_parallel_size == 0, 'current global ' \
'batch size ({}) is not divisible by micro-batch-size ({}) times' \
'data parallel size ({})'.format(self.current_global_batch_size,
self.micro_batch_size,
self.data_parallel_size)
self.num_micro_batches = self.current_global_batch_size // \
self.micro_batch_times_data_parallel_size
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