Examples

These examples will guide you through using the Intel® Extension for PyTorch* on Intel CPUs.

You can also refer to the Features section to get the examples and usage instructions related to particular features.

The source code for these examples, as well as the feature examples, can be found in the GitHub source tree under the examples directory.

• Python examples demonstrate usage of Python APIs:

  • Training

  • Inference

• C++ examples demonstrate usage of C++ APIs

• Intel® AI Reference Models provide out-of-the-box use cases, demonstrating the performance benefits achievable with Intel Extension for PyTorch*

Prerequisites: Before running these examples, please note the following:

• To run the examples, install the torchvision and transformers Python packages.

• Examples using the BFloat16 data type require machines with the Intel® Advanced Vector Extensions 512 (Intel® AVX-512) BF16 and Intel® Advanced Matrix Extensions (Intel® AMX) BF16 instruction sets.

Python

Training

Single-instance Training

To use Intel® Extension for PyTorch* on training, you need to make the following changes in your code:

1. Import intel_extension_for_pytorch as ipex.

2. Invoke the ipex.optimize function to apply optimizations against the model and optimizer objects, as shown below:

...
import torch
import intel_extension_for_pytorch as ipex
...
model = Model()
criterion = ...
optimizer = ...
model.train()
# For Float32
model, optimizer = ipex.optimize(model, optimizer=optimizer)
# For BFloat16
model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=torch.bfloat16)
# Invoke the code below to enable experimental feature torch.compile
model = torch.compile(model, backend="ipex")
...
optimizer.zero_grad()
output = model(data)
...

Below you can find complete code examples demonstrating how to use the extension on training for different data types:

Float32

import torch
import torchvision
import intel_extension_for_pytorch as ipex

LR = 0.001
DOWNLOAD = True
DATA = 'datasets/cifar10/'

transform = torchvision.transforms.Compose([
    torchvision.transforms.Resize((224, 224)),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = torchvision.datasets.CIFAR10(
    root=DATA,
    train=True,
    transform=transform,
    download=DOWNLOAD,
)
train_loader = torch.utils.data.DataLoader(
    dataset=train_dataset,
    batch_size=128
)

model = torchvision.models.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9)
model.train()

model, optimizer = ipex.optimize(model, optimizer=optimizer)
# Uncomment the code below to enable experimental feature torch.compile
# model = torch.compile(model, backend="ipex")

for batch_idx, (data, target) in enumerate(train_loader):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()
    print(batch_idx)

torch.save({
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
}, 'checkpoint.pth')

print("Execution finished")

BFloat16

import torch
import torchvision
import intel_extension_for_pytorch as ipex

LR = 0.001
DOWNLOAD = True
DATA = 'datasets/cifar10/'

transform = torchvision.transforms.Compose([
    torchvision.transforms.Resize((224, 224)),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = torchvision.datasets.CIFAR10(
    root=DATA,
    train=True,
    transform=transform,
    download=DOWNLOAD,
)
train_loader = torch.utils.data.DataLoader(
    dataset=train_dataset,
    batch_size=128
)

model = torchvision.models.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9)
model.train()

model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=torch.bfloat16)
# Uncomment the code below to enable experimental feature torch.compile
# model = torch.compile(model, backend="ipex")

for batch_idx, (data, target) in enumerate(train_loader):
    optimizer.zero_grad()
    with torch.cpu.amp.autocast():
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
    optimizer.step()
    print(batch_idx)

torch.save({
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
}, 'checkpoint.pth')

print("Execution finished")

Distributed Training

Distributed training with PyTorch DDP is accelerated by oneAPI Collective Communications Library Bindings for Pytorch* (oneCCL Bindings for Pytorch*). The extension supports FP32 and BF16 data types. More detailed information and examples are available at the Github repo.

Note: When performing distributed training with BF16 data type, use oneCCL Bindings for Pytorch*. Due to a PyTorch limitation, distributed training with BF16 data type with Intel® Extension for PyTorch* is not supported.

import os
import torch
import torch.distributed as dist
import torchvision
import oneccl_bindings_for_pytorch as torch_ccl
import intel_extension_for_pytorch as ipex

LR = 0.001
DOWNLOAD = True
DATA = 'datasets/cifar10/'

transform = torchvision.transforms.Compose([
    torchvision.transforms.Resize((224, 224)),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = torchvision.datasets.CIFAR10(
        root=DATA,
        train=True,
        transform=transform,
        download=DOWNLOAD,
)
train_loader = torch.utils.data.DataLoader(
        dataset=train_dataset,
        batch_size=128
)

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://'
)

model = torchvision.models.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr = LR, momentum=0.9)
model.train()
model, optimizer = ipex.optimize(model, optimizer=optimizer)

model = torch.nn.parallel.DistributedDataParallel(model)

for batch_idx, (data, target) in enumerate(train_loader):
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()
    print('batch_id: {}'.format(batch_idx))
torch.save({
     'model_state_dict': model.state_dict(),
     'optimizer_state_dict': optimizer.state_dict(),
     }, 'checkpoint.pth')

Inference

The optimize function of Intel® Extension for PyTorch* applies optimizations to the model, bringing additional performance boosts. For both computer vision workloads and NLP workloads, we recommend applying the optimize function against the model object.

Float32

Imperative Mode

Resnet50

import torch
import torchvision.models as models

model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
######################################################  # noqa F401

with torch.no_grad():
    model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
######################################################  # noqa F401

with torch.no_grad():
    model(data)

print("Execution finished")

TorchScript Mode

We recommend using Intel® Extension for PyTorch* with TorchScript for further optimizations.

Resnet50

import torch
import torchvision.models as models

model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
######################################################  # noqa F401

with torch.no_grad():
    d = torch.rand(1, 3, 224, 224)
    model = torch.jit.trace(model, d)
    model = torch.jit.freeze(model)

    model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
######################################################  # noqa F401

with torch.no_grad():
    d = torch.randint(vocab_size, size=[batch_size, seq_length])
    model = torch.jit.trace(model, (d,), check_trace=False, strict=False)
    model = torch.jit.freeze(model)

    model(data)

print("Execution finished")

TorchDynamo Mode (Experimental, NEW feature from 2.0.0)

Resnet50

import torch
import torchvision.models as models

model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model.eval()
data = torch.rand(1, 3, 224, 224)

# Experimental Feature
#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, weights_prepack=False)
model = torch.compile(model, backend="ipex")
######################################################  # noqa F401

with torch.no_grad():
    model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

# Experimental Feature
#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, weights_prepack=False)
model = torch.compile(model, backend="ipex")
######################################################  # noqa F401

with torch.no_grad():
    model(data)

print("Execution finished")

BFloat16

The optimize function works for both Float32 and BFloat16 data type. For BFloat16 data type, set the dtype parameter to torch.bfloat16. We recommend using Auto Mixed Precision (AMP) with BFloat16 data type.

Imperative Mode

Resnet50

import torch
import torchvision.models as models

model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, dtype=torch.bfloat16)
######################################################  # noqa F401

with torch.no_grad(), torch.cpu.amp.autocast():
    model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, dtype=torch.bfloat16)
######################################################  # noqa F401

with torch.no_grad(), torch.cpu.amp.autocast():
    model(data)

print("Execution finished")

TorchScript Mode

We recommend using Intel® Extension for PyTorch* with TorchScript for further optimizations.

Resnet50

import torch
import torchvision.models as models

model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, dtype=torch.bfloat16)
######################################################  # noqa F401

with torch.no_grad(), torch.cpu.amp.autocast():
    model = torch.jit.trace(model, torch.rand(1, 3, 224, 224))
    model = torch.jit.freeze(model)

    model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, dtype=torch.bfloat16)
######################################################  # noqa F401

with torch.no_grad(), torch.cpu.amp.autocast():
    d = torch.randint(vocab_size, size=[batch_size, seq_length])
    model = torch.jit.trace(model, (d,), check_trace=False, strict=False)
    model = torch.jit.freeze(model)

    model(data)

print("Execution finished")

TorchDynamo Mode (Experimental, NEW feature from 2.0.0)

Resnet50

import torch
import torchvision.models as models

model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)

# Experimental Feature
#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, dtype=torch.bfloat16, weights_prepack=False)
model = torch.compile(model, backend="ipex")
######################################################  # noqa F401

with torch.no_grad(), torch.cpu.amp.autocast():
    model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

# Experimental Feature
#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model, dtype=torch.bfloat16)
model = torch.compile(model, backend="ipex")
######################################################  # noqa F401

with torch.no_grad(), torch.cpu.amp.autocast():
    model(data)

print("Execution finished")

Fast Bert (Experimental)

import torch
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])
torch.manual_seed(43)

#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
model = ipex.fast_bert(model, dtype=torch.bfloat16)
######################################################  # noqa F401

with torch.no_grad():
    model(data)

print("Execution finished")

INT8

Starting from Intel® Extension for PyTorch* 1.12.0, quantization feature supports both static and dynamic modes.

Calibration

Static Quantization

Please follow the steps below to perform static calibration:

1. Import intel_extension_for_pytorch as ipex.

2. Import prepare and convert from intel_extension_for_pytorch.quantization.

3. Instantiate a config object from torch.ao.quantization.QConfig to save configuration data during calibration.

4. Prepare model for calibration.

5. Perform calibration against dataset.

6. Invoke ipex.quantization.convert function to apply the calibration configure object to the fp32 model object to get an INT8 model.

7. Save the INT8 model into a pt file.

import torch
#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
from intel_extension_for_pytorch.quantization import prepare, convert
######################################################  # noqa F401

##### Example Model #####  # noqa F401
import torchvision.models as models
model = models.resnet50(weights='ResNet50_Weights.DEFAULT')
model.eval()
data = torch.rand(1, 3, 224, 224)
#########################  # noqa F401

qconfig_mapping = ipex.quantization.default_static_qconfig_mapping
# Alternatively, define your own qconfig_mapping:
# from torch.ao.quantization import MinMaxObserver, PerChannelMinMaxObserver, QConfig, QConfigMapping
# qconfig = QConfig(
#        activation=MinMaxObserver.with_args(qscheme=torch.per_tensor_affine, dtype=torch.quint8),
#        weight=PerChannelMinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
# qconfig_mapping = QConfigMapping().set_global(qconfig)
prepared_model = prepare(model, qconfig_mapping, example_inputs=data, inplace=False)

##### Example Dataloader #####  # noqa F401
import torchvision
DOWNLOAD = True
DATA = 'datasets/cifar10/'

transform = torchvision.transforms.Compose([
    torchvision.transforms.Resize((224, 224)),
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = torchvision.datasets.CIFAR10(
    root=DATA,
    train=True,
    transform=transform,
    download=DOWNLOAD,
)
calibration_data_loader = torch.utils.data.DataLoader(
    dataset=train_dataset,
    batch_size=128
)

with torch.no_grad():
    for batch_idx, (d, target) in enumerate(calibration_data_loader):
        print(f'calibrated on batch {batch_idx} out of {len(calibration_data_loader)}')
        prepared_model(d)
##############################  # noqa F401

converted_model = convert(prepared_model)
with torch.no_grad():
    traced_model = torch.jit.trace(converted_model, data)
    traced_model = torch.jit.freeze(traced_model)

traced_model.save("quantized_model.pt")

print("Execution finished")

Dynamic Quantization

Please follow the steps below to perform static calibration:

1. Import intel_extension_for_pytorch as ipex.

2. Import prepare and convert from intel_extension_for_pytorch.quantization.

3. Instantiate a config object from torch.ao.quantization.QConfig to save configuration data during calibration.

4. Prepare model for quantization.

5. Convert the model.

6. Run inference to perform dynamic quantization.

7. Save the INT8 model into a pt file.

import torch
#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex
from intel_extension_for_pytorch.quantization import prepare, convert
######################################################  # noqa F401

##### Example Model #####  # noqa F401
from transformers import BertModel

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])
#########################  # noqa F401

qconfig_mapping = ipex.quantization.default_dynamic_qconfig_mapping
# Alternatively, define your own qconfig:
# from torch.ao.quantization import PerChannelMinMaxObserver, PlaceholderObserver, QConfig, QConfigMapping
# qconfig = QConfig(
#        activation = PlaceholderObserver.with_args(dtype=torch.float, is_dynamic=True),
#        weight = PerChannelMinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
# qconfig_mapping = QConfigMapping().set_global(qconfig)
prepared_model = prepare(model, qconfig_mapping, example_inputs=data)

converted_model = convert(prepared_model)
with torch.no_grad():
    traced_model = torch.jit.trace(converted_model, (data,), check_trace=False, strict=False)
    traced_model = torch.jit.freeze(traced_model)

traced_model.save("quantized_model.pt")

print("Execution finished")

Deployment

For deployment, the INT8 model is loaded from the local file and can be used directly on the inference.

Follow the steps below:

1. Import intel_extension_for_pytorch as ipex.

2. Load the INT8 model from the saved file.

3. Run inference.

import torch
#################### code changes ####################  # noqa F401
import intel_extension_for_pytorch as ipex              # noqa F401
######################################################  # noqa F401

model = torch.jit.load('quantized_model.pt')
model.eval()
model = torch.jit.freeze(model)
data = torch.rand(1, 3, 224, 224)

with torch.no_grad():
    model(data)

print("Execution finished")

oneDNN provides oneDNN Graph Compiler as a prototype feature that could boost performance for selective topologies. No code change is required. Install a binary with this feature enabled. We verified this feature with Bert-large, bert-base-cased, roberta-base, xlm-roberta-base, google-electra-base-generator and google-electra-base-discriminator.

C++

To work with libtorch, C++ library of PyTorch, Intel® Extension for PyTorch* provides its C++ dynamic library as well. The C++ library is supposed to handle inference workload only, such as service deployment. For regular development, use the Python interface. Unlike using libtorch, no specific code changes are required. Compilation follows the recommended methodology with CMake. Detailed instructions can be found in PyTorch tutorial.

During compilation, Intel optimizations will be activated automatically once C++ dynamic library of Intel® Extension for PyTorch* is linked.

The example code below works for all data types.

example-app.cpp

#include <torch/script.h>
#include <iostream>
#include <memory>

int main(int argc, const char* argv[]) {
  torch::jit::script::Module module;
  try {
    module = torch::jit::load(argv[1]);
  } catch (const c10::Error& e) {
    std::cerr << "error loading the model\n";
    return -1;
  }

  std::vector<torch::jit::IValue> inputs;
  torch::Tensor input = torch::rand({1, 3, 224, 224});
  inputs.push_back(input);

  at::Tensor output = module.forward(inputs).toTensor();
  std::cout << output.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << std::endl;
  std::cout << "Execution finished" << std::endl;

  return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(example-app)

find_package(IPEX REQUIRED)

add_executable(example-app example-app.cpp)
target_link_libraries(example-app "${TORCH_IPEX_LIBRARIES}")

set_property(TARGET example-app PROPERTY CXX_STANDARD 17)

Command for compilation

$ cd examples/cpu/inference/cpp
$ mkdir build
$ cd build
$ cmake -DCMAKE_PREFIX_PATH=<LIBPYTORCH_PATH> ..
$ make

If Found IPEX is shown as with a dynamic library path, the extension had been linked into the binary. This can be verified with Linux command ldd.

$ cmake -DCMAKE_PREFIX_PATH=/workspace/libtorch ..
-- The C compiler identification is GNU XX.X.X
-- The CXX compiler identification is GNU XX.X.X
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
CMake Warning at /workspace/libtorch/share/cmake/Torch/TorchConfig.cmake:22 (message):
  static library kineto_LIBRARY-NOTFOUND not found.
Call Stack (most recent call first):
  /workspace/libtorch/share/cmake/Torch/TorchConfig.cmake:127 (append_torchlib_if_found)
  /workspace/libtorch/share/cmake/IPEX/IPEXConfig.cmake:84 (FIND_PACKAGE)
  CMakeLists.txt:4 (find_package)


-- Found Torch: /workspace/libtorch/lib/libtorch.so
-- Found IPEX: /workspace/libtorch/lib/libintel-ext-pt-cpu.so
-- Configuring done
-- Generating done
-- Build files have been written to: examples/cpu/inference/cpp/build

$ ldd example-app
        ...
        libtorch.so => /workspace/libtorch/lib/libtorch.so (0x00007f3cf98e0000)
        libc10.so => /workspace/libtorch/lib/libc10.so (0x00007f3cf985a000)
        libintel-ext-pt-cpu.so => /workspace/libtorch/lib/libintel-ext-pt-cpu.so (0x00007f3cf70fc000)
        libtorch_cpu.so => /workspace/libtorch/lib/libtorch_cpu.so (0x00007f3ce16ac000)
        ...
        libdnnl_graph.so.0 => /workspace/libtorch/lib/libdnnl_graph.so.0 (0x00007f3cde954000)
        ...

Intel® AI Reference Models

Use cases that have already been optimized by Intel engineers are available at Intel® AI Reference Models (former Model Zoo). A number of PyTorch use cases for benchmarking are also available in the benchmarks. You can get performance benefits out-of-the-box by simply running scripts in the Intel® AI Reference Models.