Pipelining AI/ML Training Workloads with CUDA Streams

ninth in our collection on performance profiling and optimization in PyTorch aimed toward emphasizing the important function of efficiency evaluation and optimization in machine studying improvement. All through the collection we now have reviewed all kinds of sensible instruments and strategies for analyzing and boosting the runtime efficiency of PyTorch-based AI/ML fashions. Our aim has been twofold:

1. To emphasise the significance of routine analysis and optimization of AI/ML workloads.
2. To reveal the accessibility of all kinds instruments and strategies for analyzing and optimizing AI/ML runtime efficiency. You don’t have to be a CUDA skilled to meaningfully enhance your mannequin efficiency and cut back compute prices.

On this submit, we’ll discover the usage of CUDA streams, a strong function of NVIDIA’s CUDA programming mannequin that provides a classy technique of overlapping GPU operations and operating them concurrently. Though we usually affiliate our AI/ML mannequin coaching workload with a single monolithic (a.okay.a., “unbreakable”) computation graph G operating on the GPU, there are some eventualities the place the graph will be decomposed into two distinct subgraphs G1 and G2, the place G=G2*G1. In such instances CUDA streams allow “pipelining” the computation graph, i.e., programming our coaching step to run G1 (on batch enter n+1) in parallel to G2 (on the nth output of G1). This method is very helpful when:

• Neither subgraph totally makes use of the GPU when run alone, and
• The 2 subgraphs are of comparable computational price (i.e., neither dominates runtime).

We are going to discover two widespread eventualities the place “pipelining” is possible:

1. Partial-model coaching or finetuning:
   It’s widespread to freeze a pre-trained mannequin spine (e.g., function extractor or encoder) and prepare solely a mannequin head (e.g., decoder). Because the frozen spine doesn’t depend on gradients from the head, the 2 will be executed concurrently.
2. Offloading knowledge preprocessing to the GPU:
   A typical technique for addressing bottlenecks within the enter pipeline (also referred to as GPU hunger), knowledge preprocessing will be moved to the GPU. Whereas prepending preprocessing operations to the mannequin graph improves efficiency, further positive aspects will be achieved by operating preprocessing on a separate CUDA stream in parallel with mannequin execution—assuming preprocessing isn’t trivial in comparison with mannequin compute.

To facilitate our dialogue, we’ll outline two toy coaching scripts and measure the coaching efficiency beneath completely different eventualities. The experiments have been run on an Amazon EC2 g5.2xlarge occasion (containing an NVIDIA A10G GPU and eight vCPUs) operating a PyTorch (2.6) Deep Learning AMI (DLAMI).

Please observe: the code snippets that we share are for demonstration functions solely —please don’t depend on their correctness or optimality. The affect of utilizing CUDA streams will differ relying on mannequin structure and system configuration. We encourage you to conduct your individual profiling and experimentation earlier than integrating CUDA streams (or another device method we check with) into your workflow.

Half 1: Pipelining an Encoder-Decoder Mannequin

The primary use-case we discover entails a CNN-based picture segmentation mannequin consisting of a set (pre-trained) encoder and a trainable decoder. On this state of affairs, because the encoder weights are frozen and unaffected by backpropagation, the encoder will be executed independently of the decoder’s coaching. On this part, we assess the affect of pipelining the coaching course of utilizing CUDA streams.

A Toy Picture Segmentation Coaching Experiment

We start by defining a easy CNN-based picture encoder together with its corresponding decoder.

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Subsequent, we assemble an artificial dataset of random photographs and segmentation maps.

from torch.utils.knowledge import DataLoader
from torchvision.datasets.imaginative and prescient import VisionDataset

# A dataset with random photographs and per-pixel labels
class FakeDataset(VisionDataset):
    def __init__(self):
        tremendous().__init__(root=None)
        self.measurement = 1000000

    def __getitem__(self, index):
        # create a random picture
        img = torch.randint(0, 256, (3, img_size, img_size),
                            dtype=torch.uint8)

        # create a random label map
        goal = torch.randint(0, num_classes, (img_size, img_size))

        return img, goal

    def __len__(self):
        return self.measurement

train_set = FakeDataset()

train_loader = DataLoader(
    dataset=train_set,
    batch_size=8,
    num_workers=8
)

Lastly, we outline the loss operate, optimizer, and coaching loop. Observe, that we freeze the encoder’s weights and prepare solely the decoder.

import time

machine = torch.machine("cuda")
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(decoder.parameters())

# Freeze the encoder weights
encoder.requires_grad_(False)
encoder.eval().to(machine)

decoder.prepare().to(machine)

warmup = 10
active_batches = 100
total_iters = warmup + active_batches

for idx, knowledge in enumerate(train_loader):
    inputs = knowledge[0].to(machine=machine, non_blocking=True).float()
    labels = knowledge[1].to(machine=machine, non_blocking=True)
    optimizer.zero_grad()
    with torch.no_grad():
        options = encoder(inputs)
    output = decoder(options)
    loss = criterion(output, labels)
    loss.backward()
    optimizer.step()

    if idx == warmup:
        # sync the GPU and begin the timer
        torch.cuda.synchronize()
        t0 = time.perf_counter()

    if idx == total_iters:
        break

# await the GPU to finnish after which cease the timer
torch.cuda.synchronize()
total_time = time.perf_counter() - t0
print(f'throughput: {active_batches / total_time}')

Our baseline coaching script achieves a mean throughput of 83 steps per second, with a mean GPU utilization of 85%.

Pipelining the Mannequin Execution With CUDA Streams

Within the revised model of the coaching loop proven under, we introduce two CUDA streams: one for executing the encoder and one for coaching the decoder. In every iteration, we carry out two operations concurrently:

1. Practice the decoder utilizing the picture options and labels from batch N.
2. Execute the encoder on enter batch N+1 to generate its picture options.

encoder_stream = torch.cuda.Stream()
decoder_stream = torch.cuda.Stream()

# initialize the options to None
options = None

for idx, knowledge in enumerate(train_loader):
    inputs = knowledge[0].to(machine, non_blocking=True).float()
    labels_next = knowledge[1].to(machine, non_blocking=True)

    if options just isn't None:
        with torch.cuda.stream(decoder_stream):
            decoder_stream.wait_stream(encoder_stream)

            optimizer.zero_grad()
            output = decoder(options)
            loss = criterion(output, labels)
            loss.backward()
            optimizer.step()

    with torch.cuda.stream(encoder_stream):
        with torch.no_grad():
            options =  encoder(inputs)
        # Report that options was produced on s1_backbone
        options.record_stream(encoder_stream)

    labels = labels_next

    if idx == warmup:
        # sync the GPU and begin the timer
        torch.cuda.synchronize()
        t0 = time.perf_counter()
    if idx == total_iters:
        break

# await the GPU to complete after which cease the timer
torch.cuda.synchronize()
total_time = time.perf_counter() - t0
print(f'throughput: {active_batches / total_time}')

This modification yields a mean throughput of 91 steps per second, representing a 9.6% speedup. This can be a vital enchancment — particularly contemplating that our baseline already had excessive GPU utilization (85%).

Sensitivity of Pipelining to Workload Properties

The effectiveness of pipelining with CUDA streams is extremely depending on the specifics of the coaching workload and runtime surroundings. If the encoder is considerably bigger than the decoder (or vice versa), pipelining could provide little profit and even hinder efficiency. Conversely, when the GPU is underutilized, pipelining tends to yield extra substantial positive aspects.

For instance this dependency, we reran the experiment with various batch sizes. The outcomes are summarized under:

Because the batch measurement will increase, the advantage of pipelining diminishes. That is seemingly as a result of bigger batch sizes naturally result in greater (and extra environment friendly) GPU utilization, leaving much less room for enchancment by means of concurrent execution.

Half 2: Offloading Augmentations onto the GPU

On this part, we’ll apply the usage of CUDA streams to the acceleration of information augmentation. In earlier weblog posts (e.g., here and here), we now have studied the issue of bottlenecks on the information enter pipeline from completely different views and reviewed a number of strategies for diagnosing and addressing them. A typical causes of those bottlenecks is CPU useful resource exhaustion, the place the CPU can not meet the computational calls for of the preprocessing pipeline. The result’s GPU hunger — a state of affairs through which the costly GPU sits idle, ready for knowledge to reach.

One efficient resolution is to dump heavy knowledge preprocessing to the GPU. We are going to reveal this method and take it a step additional by executing the augmentations on a devoted CUDA stream, enabling concurrent execution with the mannequin coaching.

A Toy Picture Classification Coaching Experiment

We start by defining a easy CNN-based picture classification mannequin:

import torch
import torch.nn as nn

import torch
import torch.nn as nn

img_size = 256
num_classes = 10
mannequin = nn.Sequential(
    # Begin with 256x256 picture
    nn.Conv2d(3, 16, kernel_size=1),
    nn.ReLU(inplace=True),
    nn.Conv2d(16, 32, kernel_size=2, stride=2),  # 2x downsample
    nn.ReLU(inplace=True),
    nn.Conv2d(32, 64, kernel_size=2, stride=2),  # 4x downsample
    nn.ReLU(inplace=True),
    nn.Conv2d(64, 128, kernel_size=2, stride=2),  # 8x downsample
    nn.ReLU(inplace=True),
    nn.Conv2d(128, 256, kernel_size=2, stride=2),  # 16x downsample
    nn.ReLU(inplace=True),
    nn.Conv2d(256, 512, kernel_size=2, stride=2),  # 32x downsample
    nn.ReLU(inplace=True),
    nn.Conv2d(512, 1024, kernel_size=2, stride=2),  # 64x downsample
    nn.ReLU(inplace=True),
    nn.Conv2d(1024, 2048, kernel_size=2, stride=2),  # 128X downsample
    nn.ReLU(inplace=True),
    nn.Conv2d(2048, 4096, kernel_size=2, stride=2),  # 256X
    nn.Flatten(),
    nn.Linear(4096, num_classes)
)

Subsequent, we create an artificial dataset with an augmentation pipeline deliberately designed to trigger a extreme efficiency bottleneck:

import random
from torch.utils.knowledge import DataLoader
import torchvision.transforms.v2 as T
from torchvision.datasets.imaginative and prescient import VisionDataset
import torchvision.transforms.v2.practical as F
import torchvision.ops as ops

# A dataset with random photographs and labels
class FakeDataset(VisionDataset):
    def __init__(self, remodel = None):
        tremendous().__init__(root=None, remodel=remodel)
        self.measurement = 1000000

    def __getitem__(self, index):
        # create a random picture
        img = torch.randint(0, 256, (3, img_size, img_size),
                           dtype=torch.uint8)
        # create a random label
        goal = torch.randint(0, num_classes, (1, ))

        if self.remodel:
            # Apply tranformations
            img = self.remodel(img)

        return img, goal

    def __len__(self):
        return self.measurement

augmentations = T.Compose([
    T.ToDtype(torch.float32),
    T.RandomCrop(img_size//2),
    T.Resize(img_size),
    T.RandomRotation(degrees=45.0),
    T.GaussianBlur(kernel_size=7),
    T.Normalize(mean=[0, 0, 0], std=[1, 1, 1])
])

train_set = FakeDataset(remodel=augmentations)

train_loader = DataLoader(
    dataset=train_set,
    batch_size=32,
    num_workers=8
)

Lastly, we outline the loss operate, optimizer, and coaching loop:

import time

machine = torch.machine("cuda")
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(mannequin.parameters())

mannequin.prepare().to(machine)

warmup = 10
active_batches = 100
total_iters = warmup + active_batches

for idx, knowledge in enumerate(train_loader):
    inputs = knowledge[0].to(machine=machine, non_blocking=True)
    labels = knowledge[1].to(machine=machine, non_blocking=True).squeeze()
    optimizer.zero_grad()
    output = mannequin(inputs)
    loss = criterion(output, labels)
    loss.backward()
    optimizer.step()

    if idx == warmup:
        # sync the GPU and begin the timer
        torch.cuda.synchronize()
        t0 = time.perf_counter()

    if idx == total_iters:
        break

# await the GPU to finnish after which cease the timer
torch.cuda.synchronize()
total_time = time.perf_counter() - t0
print(f'throughput: {active_batches / total_time}')

Working this baseline script leads to a mean throughput of 20.41 steps per second and a GPU utilization of solely 42%. The heavy knowledge augmentations are choking the CPU resulting in GPU hunger. See our previous post for extra info on detecting bottlenecks on the information enter pipeline.

Offloading Information Augmentations to the GPU

To handle the efficiency bottleneck on the information enter pipeline, we transfer the augmentations onto the GPU.

Step one is to outline custom data transforms that apply random rotations and crops per pattern in a batch. That is essential as a result of the built-in torchvision transforms apply the identical augmentation throughout the whole batch — dropping the per-sample randomness seen on the CPU.

We implement the BatchRandomCrop remodel utilizing the roi_align operator.

class BatchRandomCrop(T.Remodel):
    def __init__(self, output_size):
        tremendous().__init__()
        self.output_size = output_size

    def remodel(self, img: torch.Tensor, params: dict):
        batch_size, _, original_height, original_width = img.form
        machine = img.machine
        max_top = original_height - self.output_size
        max_left = original_width - self.output_size

        # Generate random high and left coords for every picture within the batch
        random_top = torch.randint(0, max_top + 1, (batch_size,),
                                   machine=machine, dtype=torch.float32)
        random_left = torch.randint(0, max_left + 1, (batch_size,),
                                    machine=machine, dtype=torch.float32)

        image_indices = torch.arange(batch_size, machine=machine,
                                     dtype=torch.float32)

        packing containers = torch.stack([
            image_indices,
            random_left,
            random_top,
            random_left + self.output_size,
            random_top + self.output_size
        ], dim=1)

        cropped_batch = ops.roi_align(
            img,
            packing containers,
            output_size=self.output_size
        )
        return cropped_batch 

We implement the BatchRandomRotate transfrom by iterating over the entire photographs within the batch and making use of a random rotation to every one. Observe that this model just isn’t vectorized; a totally vectorized implementation could be extra would require better effort.

class BatchRandomRotation(T.Remodel):
    def __init__(self, levels):
        tremendous().__init__()
        self .levels = levels

    def remodel(self, inpt: torch.Tensor, params: dict):
        # cut up the batch into an inventory of particular person photographs
        photographs = record(torch.unbind(inpt, dim=0))

        augmented_images = []
        for img_tensor in photographs:
            # generate a random angle
            angle = random.uniform(-self.levels, self.levels)

            # apply the rotation to the one picture
            transformed_img = F.rotate(
                img_tensor,
                angle=angle
            )
            augmented_images.append(transformed_img)

        # stack the remodeled photographs
        return torch.stack(augmented_images, dim=0)

We now outline batch_transform that mimics the CPU-based augmentation pipeline outlined above:

batch_transform = T.Compose([
    T.ToDtype(torch.float32),
    BatchRandomCrop(img_size//2),
    T.Resize(img_size),
    BatchRandomRotation(degrees=45.0),
    T.GaussianBlur(kernel_size=7),
    T.Normalize(mean=[0, 0, 0], std=[1, 1, 1])
]) 

Lastly, we reset the dataset and replace the coaching loop to use the brand new batch_transform:

train_set = FakeDataset(remodel=None)

train_loader = DataLoader(
    dataset=train_set,
    batch_size=32,
    num_workers=8
)

for idx, knowledge in enumerate(train_loader):
    inputs = knowledge[0].to(machine=machine, non_blocking=True)
    labels = knowledge[1].to(machine=machine, non_blocking=True).squeeze()
    
    # apply augmentations
    inputs = batch_transform(inputs)
    
    optimizer.zero_grad()
    output = mannequin(inputs)
    loss = criterion(output, labels)
    loss.backward()
    optimizer.step()

    if idx == warmup:
        torch.cuda.synchronize()
        t0 = time.perf_counter()

    if idx == total_iters:
        break

torch.cuda.synchronize()
total_time = time.perf_counter() - t0
print(f'throughput: {active_batches / total_time}')

This up to date coaching script improves throughput to 35.22 steps per second — a 72.57% speedup over the baseline outcome.

Pipelining Augmentations With CUDA Streams

Subsequent, we pipeline the augmentation and coaching steps utilizing two separate CUDA streams: one for operating the information remodel one for coaching the mannequin. In every iteration of the loop we carry out two concurrent operations:

1. We prepare the mannequin on the augmented batch N.
2. Carry out GPU-based knowledge augmentations on batch N+1

transform_stream = torch.cuda.Stream()
model_stream = torch.cuda.Stream()

# initialize the remodeled worth to None
remodeled = None

for idx, knowledge in enumerate(train_loader):
    inputs = knowledge[0]
    labels_next = knowledge[1]

    if remodeled just isn't None:
        with torch.cuda.stream(model_stream):
            labels = labels.to(machine, non_blocking=True).squeeze()
            model_stream.wait_stream(transform_stream)
            optimizer.zero_grad()
            output = mannequin(remodeled)
            loss = criterion(output, labels)
            loss.backward()
            optimizer.step()

    with torch.cuda.stream(transform_stream):
        inputs = inputs.to(machine, non_blocking=True)
        remodeled = batch_transform(inputs)
        # Report that the tensor was produced on transform_stream
        remodeled.record_stream(transform_stream)

    labels = labels_next

    if idx == warmup:
        torch.cuda.synchronize()
        t0 = time.perf_counter()
    if idx == total_iters:
        break

torch.cuda.synchronize()
total_time = time.perf_counter() - t0
print(f'throughput: {active_batches / total_time}')

This additional improves the throughput to 38.82 steps per second — a ten.2% enhance over the serialized resolution, and 90.20% sooner than the unique baseline

Sensitivity of Pipelining to Workload Properties

As we noticed in Half 1, the advantage of pipelining utilizing CUDA streams varies primarily based on the small print of the workload. Within the desk under, we seize the outcomes for a number of completely different batch sizes:

Because the batch measurement will increase, GPU offloading turns into more practical, considerably boosting efficiency. On the identical time, the positive aspects from pipelining lower. That is seemingly do to the actual fact bigger batch sizes enhance the GPU effectivity, decreasing the alternatives for overlap.

Abstract

In relation to operating AI/ML workloads, each millisecond counts. On this submit we explored the affect of pipelining an AI/ML coaching step utilizing CUDA stream in two widespread eventualities: partial mannequin coaching and offloading knowledge augmentations to the GPU. In each instances, the pipelined resolution outperformed the serialized implementation — although the extent of the advance various considerably primarily based on the worth of the batch measurement.

As we’ve emphasised all through the submit, the anticipated affect of the usage of CUDA streams can differ tremendously primarily based on the AI/ML workload. For instance, in instances the place the GPU is already being effectively utilized, the overhead of utilizing CUDA streams may very well result in a degradation in runtime efficiency. We strongly suggest testing this method by yourself workloads earlier than adopting this method.

We hope you will see that the method described on this submit helpful. For extra tip, tips, and strategies for profiling and optimizing AI/ML workflows, take a look at the opposite posts on this series.