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.
For demonstration purposes we will use synthetic data loader.
import os import logging import warnings import time import numpy as np 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 devices device = [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.np.random.uniform(-1, 1, size=shape, device=mx.cpu_pinned()) self.cls_targets = mx.np.random.uniform(0, 1, size=cls_targets_shape, device=mx.cpu_pinned()) self.box_targets = mx.np.random.uniform(0, 1, size=box_targets_shape, device=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.reset_device(device) return net
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=device, batch_axis=0) cls_targets = gluon.utils.split_and_load(batch[1], ctx_list=device, batch_axis=0) box_targets = gluon.utils.split_and_load(batch[2], ctx_list=device, 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()
output
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
In order to start using AMP, we need to import and initialize it. This has to happen before we create the network.
from mxnet import amp amp.init()
output:
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)
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=device, batch_axis=0) cls_targets = gluon.utils.split_and_load(batch[1], ctx_list=device, batch_axis=0) box_targets = gluon.utils.split_and_load(batch[2], ctx_list=device, 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()
output
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!
To do inference with mixed precision for a trained model in FP32, you can use the conversion API amp.convert_hybrid_block for gluon models. The conversion APIs will take the FP32 model as input and will return a mixed precision model, which can be used to run inference. Below, we demonstrate for a gluon model:
with mx.Context(mx.gpu(0)): # Below is an example of converting a gluon hybrid block to a mixed precision block with warnings.catch_warnings(record=True) as w: warnings.simplefilter("ignore") model = get_model("resnet50_v1") model.initialize(device=mx.current_device()) model.hybridize() model(mx.np.zeros((1, 3, 224, 224))) converted_model = amp.convert_hybrid_block(model) # Run dummy inference with the converted gluon model result = converted_model.forward(mx.np.random.uniform(size=(1, 3, 224, 224), dtype=np.float32)) print("Conversion and Inference completed successfully")
You can also customize the operators to run in FP16 versus the operator to run in FP32 or to conditionally run in FP32. Also, you can force cast the params wherever possible to FP16.
update_on_kvstore=False option set