Coaching AI Fashions on CPU. Revisiting CPU for ML in an Period of GPU… | by Chaim Rand | Sep, 2024

The current successes in AI are sometimes attributed to the emergence and evolutions of the GPU. The GPU’s structure, which generally contains hundreds of multi-processors, high-speed reminiscence, devoted tensor cores, and extra, is especially well-suited to satisfy the intensive calls for of AI/ML workloads. Sadly, the fast development in AI improvement has led to a surge within the demand for GPUs, making them tough to acquire. In consequence, ML builders are more and more exploring various {hardware} choices for coaching and operating their fashions. In earlier posts, we mentioned the potential for coaching on devoted AI ASICs reminiscent of Google Cloud TPU, Haban Gaudi, and AWS Trainium. Whereas these choices supply important cost-saving alternatives, they don’t go well with all ML fashions and might, just like the GPU, additionally undergo from restricted availability. On this submit we return to the great old style CPU and revisit its relevance to ML purposes. Though CPUs are typically much less suited to ML workloads in comparison with GPUs, they’re much simpler to amass. The flexibility to run (at the very least a few of) our workloads on CPU may have important implications on improvement productiveness.

In earlier posts (e.g., right here) we emphasised the significance of analyzing and optimizing the runtime efficiency of AI/ML workloads as a method of accelerating improvement and minimizing prices. Whereas that is essential whatever the compute engine used, the profiling instruments and optimization methods can fluctuate drastically between platforms. On this submit, we’ll focus on among the efficiency optimization choices that pertain to CPU. Our focus shall be on Intel® Xeon® CPU processors (with Intel® AVX-512) and on the PyTorch (model 2.4) framework (though related methods may be utilized to different CPUs and frameworks, as effectively). Extra particularly, we’ll run our experiments on an Amazon EC2 c7i occasion with an AWS Deep Studying AMI. Please don’t view our selection of Cloud platform, CPU model, ML framework, or another device or library we should always point out, as an endorsement over their options.

Our purpose shall be to show that though ML improvement on CPU is probably not our first selection, there are methods to “soften the blow” and — in some instances — maybe even make it a viable various.

Disclaimers

Our intention on this submit is to show only a few of the ML optimization alternatives obtainable on CPU. Opposite to a lot of the on-line tutorials on the subject of ML optimization on CPU, we’ll deal with a coaching workload relatively than an inference workload. There are a selection of optimization instruments centered particularly on inference that we’ll not cowl (e.g., see right here and right here).

Please don’t view this submit as a substitute of the official documentation on any of the instruments or methods that we point out. Take into account that given the fast tempo of AI/ML improvement, among the content material, libraries, and/or directions that we point out might grow to be outdated by the point you learn this. Please you should definitely seek advice from the most-up-to-date documentation obtainable.

Importantly, the influence of the optimizations that we focus on on runtime efficiency is prone to fluctuate drastically primarily based on the mannequin and the small print of the setting (e.g., see the excessive diploma of variance between fashions on the official PyTorch TouchInductor CPU Inference Efficiency Dashboard). The comparative efficiency numbers we’ll share are particular to the toy mannequin and runtime setting that we’ll use. Make sure to reevaluate the entire proposed optimizations by yourself mannequin and runtime setting.

Lastly, our focus shall be solely on throughput efficiency (as measured in samples per second) — not on coaching convergence. Nonetheless, it needs to be famous that some optimization methods (e.g., batch measurement tuning, blended precision, and extra) may have a adverse impact on the convergence of sure fashions. In some instances, this may be overcome via applicable hyperparameter tuning.

We’ll run our experiments on a easy picture classification mannequin with a ResNet-50 spine (from Deep Residual Studying for Picture Recognition). We’ll prepare the mannequin on a pretend dataset. The complete coaching script seems within the code block beneath (loosely primarily based on this instance):

import torch
import torchvision
from torch.utils.information import Dataset, DataLoader
import time
# A dataset with random photographs and labels
class FakeDataset(Dataset):
def __len__(self):
return 1000000
def __getitem__(self, index):
rand_image = torch.randn([3, 224, 224], dtype=torch.float32)
label = torch.tensor(information=index % 10, dtype=torch.uint8)
return rand_image, label
train_set = FakeDataset()
batch_size=128
num_workers=0
train_loader = DataLoader(
dataset=train_set,
batch_size=batch_size,
num_workers=num_workers
)
mannequin = torchvision.fashions.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(mannequin.parameters())
mannequin.prepare()
t0 = time.perf_counter()
summ = 0
rely = 0
for idx, (information, goal) in enumerate(train_loader):
optimizer.zero_grad()
output = mannequin(information)
loss = criterion(output, goal)
loss.backward()
optimizer.step()
batch_time = time.perf_counter() - t0
if idx > 10:  # skip first steps
summ += batch_time
rely += 1
t0 = time.perf_counter()
if idx > 100:
break
print(f'common step time: {summ/rely}')
print(f'throughput: {rely*batch_size/summ}')

Operating this script on a c7i.2xlarge (with 8 vCPUs) and the CPU model of PyTorch 2.4, ends in a throughput of 9.12 samples per second. For the sake of comparability, we be aware that the throughput of the identical (unoptimized script) on an Amazon EC2 g5.2xlarge occasion (with 1 GPU and eight vCPUs) is 340 samples per second. Considering the comparative prices of those two occasion sorts ($0.357 per hour for a c7i.2xlarge and $1.212 for a g5.2xlarge, as of the time of this writing), we discover that coaching on the GPU occasion to offer roughly eleven(!!) occasions higher worth efficiency. Primarily based on these outcomes, the desire for utilizing GPUs to coach ML fashions could be very effectively based. Let’s assess among the prospects for lowering this hole.

On this part we’ll discover some primary strategies for rising the runtime efficiency of our coaching workload. Though you could acknowledge a few of these from our submit on GPU optimization, it is very important spotlight a big distinction between coaching optimization on CPU and GPU platforms. On GPU platforms a lot of our effort was devoted to maximizing the parallelization between (the coaching information preprocessing on) the CPU and (the mannequin coaching on) the GPU. On CPU platforms the entire processing happens on the CPU and our purpose shall be to allocate its assets most successfully.

Batch Dimension

Rising the coaching batch measurement can doubtlessly enhance efficiency by lowering the frequency of the mannequin parameter updates. (On GPUs it has the additional benefit of lowering the overhead of CPU-GPU transactions reminiscent of kernel loading). Nonetheless, whereas on GPU we aimed for a batch measurement that will maximize the utilization of the GPU reminiscence, the identical technique would possibly damage efficiency on CPU. For causes past the scope of this submit, CPU reminiscence is extra sophisticated and one of the best strategy for locating probably the most optimum batch measurement could also be via trial and error. Take into account that altering the batch measurement may have an effect on coaching convergence.

The desk beneath summarizes the throughput of our coaching workload for a couple of (arbitrary) decisions of batch measurement:

Opposite to our findings on GPU, on the c7i.2xlarge occasion kind our mannequin seems to favor decrease batch sizes.

Multi-process Knowledge Loading

A typical method on GPUs is to assign a number of processes to the information loader in order to scale back the chance of hunger of the GPU. On GPU platforms, a common rule of thumb is to set the variety of staff in line with the variety of CPU cores. Nonetheless, on CPU platforms, the place the mannequin coaching makes use of the identical assets as the information loader, this strategy may backfire. As soon as once more, one of the best strategy for selecting the optimum variety of staff could also be trial and error. The desk beneath reveals the typical throughput for various decisions of num_workers:

Combined Precision

One other in style method is to make use of decrease precision floating level datatypes reminiscent of torch.float16 or torch.bfloat16 with the dynamic vary of torch.bfloat16 typically thought of to be extra amiable to ML coaching. Naturally, lowering the datatype precision can have adversarial results on convergence and needs to be carried out rigorously. PyTorch comes with torch.amp, an automated blended precision package deal for optimizing the usage of these datatypes. Intel® AVX-512 contains help for the bfloat16 datatype. The modified coaching step seems beneath:

for idx, (information, goal) in enumerate(train_loader):
optimizer.zero_grad()
with torch.amp.autocast('cpu',dtype=torch.bfloat16):
output = mannequin(information)
loss = criterion(output, goal)
loss.backward()
optimizer.step()

The throughput following this optimization is 24.34 samples per second, a rise of 86%!!

Channels Final Reminiscence Format

Channels final reminiscence format is a beta-level optimization (on the time of this writing), pertaining primarily to imaginative and prescient fashions, that helps storing 4 dimensional (NCHW) tensors in reminiscence such that the channels are the final dimension. This ends in the entire information of every pixel being saved collectively. This optimization pertains primarily to imaginative and prescient fashions. Thought-about to be extra “pleasant to Intel platforms”, this reminiscence format is reported increase the efficiency of a ResNet-50 on an Intel® Xeon® CPU. The adjusted coaching step seems beneath:

for idx, (information, goal) in enumerate(train_loader):
information = information.to(memory_format=torch.channels_last)
optimizer.zero_grad()
with torch.amp.autocast('cpu',dtype=torch.bfloat16):
output = mannequin(information)
loss = criterion(output, goal)
loss.backward()
optimizer.step()

The ensuing throughput is 37.93 samples per second — a further 56% enchancment and a complete of 415% in comparison with our baseline experiment. We’re on a job!!

Torch Compilation

In a earlier submit we lined the virtues of PyTorch’s help for graph compilation and its potential influence on runtime efficiency. Opposite to the default keen execution mode by which every operation is run independently (a.ok.a., “eagerly”), the compile API converts the mannequin into an intermediate computation graph which is then JIT-compiled into low-level machine code in a way that’s optimum for the underlying coaching engine. The API helps compilation by way of completely different backend libraries and with a number of configuration choices. Right here we’ll restrict our analysis to the default (TorchInductor) backend and the ipex backend from the Intel® Extension for PyTorch, a library with devoted optimizations for Intel {hardware}. Please see the documentation for applicable set up and utilization directions. The up to date mannequin definition seems beneath:

import intel_extension_for_pytorch as ipex
mannequin = torchvision.fashions.resnet50()
backend='inductor' # optionally change to 'ipex'
mannequin = torch.compile(mannequin, backend=backend)

Within the case of our toy mannequin, the influence of torch compilation is simply obvious when the “channels final” optimization is disabled (and enhance of ~27% for every of the backends). When “channels final” is utilized, the efficiency truly drops. In consequence, we drop this optimization from our subsequent experiments.

There are a selection of alternatives for optimizing the usage of the underlying CPU assets. These embody optimizing reminiscence administration and thread allocation to the construction of the underlying CPU {hardware}. Reminiscence administration may be improved via the usage of superior reminiscence allocators (reminiscent of Jemalloc and TCMalloc) and/or lowering reminiscence accesses which are slower (i.e., throughout NUMA nodes). Threading allocation may be improved via applicable configuration of the OpenMP threading library and/or use of Intel’s Open MP library.

Usually talking, these sorts of optimizations require a deep degree understanding of the CPU structure and the options of its supporting SW stack. To simplify issues, PyTorch affords the torch.backends.xeon.run_cpu script for routinely configuring the reminiscence and threading libraries in order to optimize runtime efficiency. The command beneath will end in the usage of the devoted reminiscence and threading libraries. We’ll return to the subject of NUMA nodes once we focus on the choice of distributed coaching.

We confirm applicable set up of TCMalloc (conda set up conda-forge::gperftools) and Intel’s Open MP library (pip set up intel-openmp), and run the next command.

python -m torch.backends.xeon.run_cpu prepare.py

Using the run_cpu script additional boosts our runtime efficiency to 39.05 samples per second. Observe that the run_cpu script contains many controls for additional tuning efficiency. Make sure to try the documentation to be able to maximize its use.

The Intel® Extension for PyTorch contains extra alternatives for coaching optimization by way of its ipex.optimize operate. Right here we show its default use. Please see the documentation to be taught of its full capabilities.

 mannequin = torchvision.fashions.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(mannequin.parameters())
mannequin.prepare()
mannequin, optimizer = ipex.optimize(
mannequin, 
optimizer=optimizer,
dtype=torch.bfloat16
)

Mixed with the reminiscence and thread optimizations mentioned above, the resultant throughput is 40.73 samples per second. (Observe {that a} related result’s reached when disabling the “channels final” configuration.)

Intel® Xeon® processors are designed with Non-Uniform Reminiscence Entry (NUMA) by which the CPU reminiscence is split into teams, a.ok.a., NUMA nodes, and every of the CPU cores is assigned to 1 node. Though any CPU core can entry the reminiscence of any NUMA node, the entry to its personal node (i.e., its native reminiscence) is way quicker. This offers rise to the notion of distributing coaching throughout NUMA nodes, the place the CPU cores assigned to every NUMA node act as a single course of in a distributed course of group and information distribution throughout nodes is managed by Intel® oneCCL, Intel’s devoted collective communications library.

We will run information distributed coaching throughout NUMA nodes simply utilizing the ipexrun utility. Within the following code block (loosely primarily based on this instance) we adapt our script to run information distributed coaching (in line with utilization detailed right here):

import os, time
import torch
from torch.utils.information import Dataset, DataLoader
from torch.utils.information.distributed import DistributedSampler
import torch.distributed as dist
import torchvision
import oneccl_bindings_for_pytorch as torch_ccl
import intel_extension_for_pytorch as ipex
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "29500"
os.environ["RANK"] = os.environ.get("PMI_RANK", "0")
os.environ["WORLD_SIZE"] = os.environ.get("PMI_SIZE", "1")
dist.init_process_group(backend="ccl", init_method="env://")
rank = os.environ["RANK"]
world_size = os.environ["WORLD_SIZE"]
batch_size = 128
num_workers = 0
# outline dataset and dataloader
class FakeDataset(Dataset):
def __len__(self):
return 1000000
def __getitem__(self, index):
rand_image = torch.randn([3, 224, 224], dtype=torch.float32)
label = torch.tensor(information=index % 10, dtype=torch.uint8)
return rand_image, label
train_dataset = FakeDataset()
dist_sampler = DistributedSampler(train_dataset)
train_loader = DataLoader(
dataset=train_dataset, 
batch_size=batch_size,
num_workers=num_workers,
sampler=dist_sampler
)
# outline mannequin artifacts
mannequin = torchvision.fashions.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(mannequin.parameters())
mannequin.prepare()
mannequin, optimizer = ipex.optimize(
mannequin, 
optimizer=optimizer,
dtype=torch.bfloat16
)
# configure DDP
mannequin = torch.nn.parallel.DistributedDataParallel(mannequin)
# run coaching loop
# destroy the method group
dist.destroy_process_group()

Sadly, as of the time of this writing, the Amazon EC2 c7i occasion household doesn’t embody a multi-NUMA occasion kind. To check our distributed coaching script, we revert again to a Amazon EC2 c6i.32xlarge occasion with 64 vCPUs and a pair of NUMA nodes. We confirm the set up of Intel® oneCCL Bindings for PyTorch and run the next command (as documented right here):

supply $(python -c "import oneccl_bindings_for_pytorch as torch_ccl;print(torch_ccl.cwd)")/env/setvars.sh
# This instance command would make the most of all of the numa sockets of the processor, taking every socket as a rank.
ipexrun cpu --nnodes 1 --omp_runtime intel prepare.py 

The next desk compares the efficiency outcomes on the c6i.32xlarge occasion with and with out distributed coaching:

In our experiment, information distribution did not increase the runtime efficiency. Please see ipexrun documentation for added efficiency tuning choices.

In earlier posts (e.g., right here) we mentioned the PyTorch/XLA library and its use of XLA compilation to allow PyTorch primarily based coaching on XLA units reminiscent of TPU, GPU, and CPU. Just like torch compilation, XLA makes use of graph compilation to generate machine code that’s optimized for the goal system. With the institution of the OpenXLA Undertaking, one of many said objectives was to help excessive efficiency throughout all {hardware} backends, together with CPU (see the CPU RFC right here). The code block beneath demonstrates the changes to our unique (unoptimized) script required to coach utilizing PyTorch/XLA:

import torch
import torchvision
import timeimport torch_xla
import torch_xla.core.xla_model as xm
system = xm.xla_device()
mannequin = torchvision.fashions.resnet50().to(system)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(mannequin.parameters())
mannequin.prepare()
for idx, (information, goal) in enumerate(train_loader):
information = information.to(system)
goal = goal.to(system)
optimizer.zero_grad()
output = mannequin(information)
loss = criterion(output, goal)
loss.backward()
optimizer.step()
xm.mark_step()

Sadly, (as of the time of this writing) the XLA outcomes on our toy mannequin appear far inferior to the (unoptimized) outcomes we noticed above (— by as a lot as 7X). We anticipate this to enhance as PyTorch/XLA’s CPU help matures.

We summarize the outcomes of a subset of our experiments within the desk beneath. For the sake of comparability, we add the throughput of coaching our mannequin on Amazon EC2 g5.2xlarge GPU occasion following the optimization steps mentioned in this submit. The samples per greenback was calculated primarily based on the Amazon EC2 On-demand pricing web page ($0.357 per hour for a c7i.2xlarge and $1.212 for a g5.2xlarge, as of the time of this writing).

Though we succeeded in boosting the coaching efficiency of our toy mannequin on the CPU occasion by a substantial margin (446%), it stays inferior to the (optimized) efficiency on the GPU occasion. Primarily based on our outcomes, coaching on GPU could be ~6.7 occasions cheaper. It’s seemingly that with extra efficiency tuning and/or making use of extra optimizations methods, we may additional shut the hole. As soon as once more, we emphasize that the comparative efficiency outcomes we now have reached are distinctive to this mannequin and runtime setting.

Amazon EC2 Spot Situations Reductions

The elevated availability of cloud-based CPU occasion sorts (in comparison with GPU occasion sorts) might suggest better alternative for acquiring compute energy at discounted charges, e.g., via Spot Occasion utilization. Amazon EC2 Spot Situations are cases from surplus cloud service capability which are supplied for a reduction of as a lot as 90% off the On-Demand pricing. In trade for the discounted worth, AWS maintains the fitting to preempt the occasion with little to no warning. Given the excessive demand for GPUs, you could discover CPU spot cases simpler to get ahold of than their GPU counterparts. On the time of this writing, c7i.2xlarge Spot Occasion worth is $0.1291 which might enhance our samples per greenback end result to 1135.76 and additional reduces the hole between the optimized GPU and CPU worth performances (to 2.43X).

Whereas the runtime efficiency outcomes of the optimized CPU coaching of our toy mannequin (and our chosen setting) have been decrease than the GPU outcomes, it’s seemingly that the identical optimization steps utilized to different mannequin architectures (e.g., ones that embody elements that aren’t supported by GPU) might end result within the CPU efficiency matching or beating that of the GPU. And even in instances the place the efficiency hole is just not bridged, there might very effectively be instances the place the scarcity of GPU compute capability would justify operating a few of our ML workloads on CPU.

Given the ubiquity of the CPU, the flexibility to make use of them successfully for coaching and/or operating ML workloads may have big implications on improvement productiveness and on end-product deployment technique. Whereas the character of the CPU structure is much less amiable to many ML purposes when in comparison with the GPU, there are numerous instruments and methods obtainable for enhancing its efficiency — a choose few of which we now have mentioned and demonstrated on this submit.

On this submit we centered optimizing coaching on CPU. Please you should definitely try our many different posts on medium protecting all kinds of subjects pertaining to efficiency evaluation and optimization of machine studying workloads.