Using AMP (Automatic Mixed Precision) in MXNet¶
Training Deep Learning networks is a very computationally intensive task. Novel model architectures tend to have increasing number of layers and parameters, which slows down training. Fortunately, new generations of training hardware as well as software optimizations, make it a feasible task.
However, where most of the (both hardware and software) optimization opportunities exists is in exploiting lower precision (like FP16) to, for example, utilize Tensor Cores available on new Volta and Turing GPUs. While training in FP16 showed great success in image classification tasks, other more complicated neural networks typically stayed in FP32 due to difficulties in applying the FP16 training guidelines.
That is where AMP (Automatic Mixed Precision) comes into play. It automatically applies the guidelines of FP16 training, using FP16 precision where it provides the most benefit, while conservatively keeping in full FP32 precision operations unsafe to do in FP16.
This tutorial shows how to get started with mixed precision training using AMP for MXNet. As an example of a network we will use SSD network from GluonCV.
Data loader and helper functions¶
For demonstration purposes we will use synthetic data loader.
import logging
import warnings
import time
import mxnet as mx
import mxnet.gluon as gluon
from mxnet import autograd
import gluoncv as gcv
from gluoncv.model_zoo import get_model
data_shape = 512
batch_size = 8
lr = 0.001
wd = 0.0005
momentum = 0.9
# training contexts
ctx = [mx.gpu(0)]
# set up logger
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
ce_metric = mx.metric.Loss('CrossEntropy')
smoothl1_metric = mx.metric.Loss('SmoothL1')
class SyntheticDataLoader(object):
def __init__(self, data_shape, batch_size):
super(SyntheticDataLoader, self).__init__()
self.counter = 0
self.epoch_size = 200
shape = (batch_size, 3, data_shape, data_shape)
cls_targets_shape = (batch_size, 6132)
box_targets_shape = (batch_size, 6132, 4)
self.data = mx.nd.random.uniform(-1, 1, shape=shape, ctx=mx.cpu_pinned())
self.cls_targets = mx.nd.random.uniform(0, 1, shape=cls_targets_shape, ctx=mx.cpu_pinned())
self.box_targets = mx.nd.random.uniform(0, 1, shape=box_targets_shape, ctx=mx.cpu_pinned())
def next(self):
if self.counter >= self.epoch_size:
self.counter = self.counter % self.epoch_size
raise StopIteration
self.counter += 1
return [self.data, self.cls_targets, self.box_targets]
__next__ = next
def __iter__(self):
return self
train_data = SyntheticDataLoader(data_shape, batch_size)
def get_network():
# SSD with RN50 backbone
net_name = 'ssd_512_resnet50_v1_coco'
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("ignore")
net = get_model(net_name, pretrained_base=True, norm_layer=gluon.nn.BatchNorm)
net.initialize()
net.collect_params().reset_ctx(ctx)
return net
Training in FP32¶
First, let us create the network.
net = get_network()
net.hybridize(static_alloc=True, static_shape=True)
Next, we need to create a Gluon Trainer.
trainer = gluon.Trainer(
net.collect_params(), 'sgd',
{'learning_rate': lr, 'wd': wd, 'momentum': momentum})
mbox_loss = gcv.loss.SSDMultiBoxLoss()
for epoch in range(1):
ce_metric.reset()
smoothl1_metric.reset()
tic = time.time()
btic = time.time()
for i, batch in enumerate(train_data):
batch_size = batch[0].shape[0]
data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0)
cls_targets = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0)
box_targets = gluon.utils.split_and_load(batch[2], ctx_list=ctx, batch_axis=0)
with autograd.record():
cls_preds = []
box_preds = []
for x in data:
cls_pred, box_pred, _ = net(x)
cls_preds.append(cls_pred)
box_preds.append(box_pred)
sum_loss, cls_loss, box_loss = mbox_loss(
cls_preds, box_preds, cls_targets, box_targets)
autograd.backward(sum_loss)
trainer.step(1)
ce_metric.update(0, [l * batch_size for l in cls_loss])
smoothl1_metric.update(0, [l * batch_size for l in box_loss])
if not (i + 1) % 50:
name1, loss1 = ce_metric.get()
name2, loss2 = smoothl1_metric.get()
logger.info('[Epoch {}][Batch {}], Speed: {:.3f} samples/sec, {}={:.3f}, {}={:.3f}'.format(
epoch, i, batch_size/(time.time()-btic), name1, loss1, name2, loss2))
btic = time.time()
INFO:root:[Epoch 0][Batch 49], Speed: 58.105 samples/sec, CrossEntropy=1.190, SmoothL1=0.688
INFO:root:[Epoch 0][Batch 99], Speed: 58.683 samples/sec, CrossEntropy=0.693, SmoothL1=0.536
INFO:root:[Epoch 0][Batch 149], Speed: 58.915 samples/sec, CrossEntropy=0.500, SmoothL1=0.453
INFO:root:[Epoch 0][Batch 199], Speed: 58.422 samples/sec, CrossEntropy=0.396, SmoothL1=0.399
Training with AMP¶
AMP initialization¶
In order to start using AMP, we need to import and initialize it. This has to happen before we create the network.
from mxnet.contrib import amp
amp.init()
INFO:root:Using AMP
After that, we can create the network exactly the same way we did in FP32 training.
net = get_network()
net.hybridize(static_alloc=True, static_shape=True)
For some models that may be enough to start training in mixed precision, but the full FP16 recipe recommends using dynamic loss scaling to guard against over- and underflows of FP16 values. Therefore, as a next step, we create a trainer and initialize it with support for AMP’s dynamic loss scaling. Currently, support for dynamic loss scaling is limited to trainers created with update_on_kvstore=False option, and so we add it to our trainer initialization.
trainer = gluon.Trainer(
net.collect_params(), 'sgd',
{'learning_rate': lr, 'wd': wd, 'momentum': momentum},
update_on_kvstore=False)
amp.init_trainer(trainer)
Dynamic loss scaling in the training loop¶
The last step is to apply the dynamic loss scaling during the training loop and . We can achieve that using the amp.scale_loss function.
mbox_loss = gcv.loss.SSDMultiBoxLoss()
for epoch in range(1):
ce_metric.reset()
smoothl1_metric.reset()
tic = time.time()
btic = time.time()
for i, batch in enumerate(train_data):
batch_size = batch[0].shape[0]
data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0)
cls_targets = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0)
box_targets = gluon.utils.split_and_load(batch[2], ctx_list=ctx, batch_axis=0)
with autograd.record():
cls_preds = []
box_preds = []
for x in data:
cls_pred, box_pred, _ = net(x)
cls_preds.append(cls_pred)
box_preds.append(box_pred)
sum_loss, cls_loss, box_loss = mbox_loss(
cls_preds, box_preds, cls_targets, box_targets)
with amp.scale_loss(sum_loss, trainer) as scaled_loss:
autograd.backward(scaled_loss)
trainer.step(1)
ce_metric.update(0, [l * batch_size for l in cls_loss])
smoothl1_metric.update(0, [l * batch_size for l in box_loss])
if not (i + 1) % 50:
name1, loss1 = ce_metric.get()
name2, loss2 = smoothl1_metric.get()
logger.info('[Epoch {}][Batch {}], Speed: {:.3f} samples/sec, {}={:.3f}, {}={:.3f}'.format(
epoch, i, batch_size/(time.time()-btic), name1, loss1, name2, loss2))
btic = time.time()
INFO:root:[Epoch 0][Batch 49], Speed: 93.585 samples/sec, CrossEntropy=1.166, SmoothL1=0.684
INFO:root:[Epoch 0][Batch 99], Speed: 93.773 samples/sec, CrossEntropy=0.682, SmoothL1=0.533
INFO:root:[Epoch 0][Batch 149], Speed: 93.399 samples/sec, CrossEntropy=0.493, SmoothL1=0.451
INFO:root:[Epoch 0][Batch 199], Speed: 93.674 samples/sec, CrossEntropy=0.391, SmoothL1=0.397
We got 60% speed increase from 3 additional lines of code!
Current limitations of AMP¶
- AMP’s dynamic loss scaling currently supports only Gluon trainer with
update_on_kvstore=Falseoption set - Using
SoftmaxOutput,LinearRegressionOutput,LogisticRegressionOutput,MAERegressionOutputwith dynamic loss scaling does not work when training networks with multiple Gluon trainers and so multiple loss scales
