目录
目录

在Intel GPU上开始使用

硬件要求

经过验证的硬件

支持的操作系统

英特尔®数据中心GPU Max系列

Linux

英特尔客户端GPU

Windows/Linux

英特尔GPU支持（原型）已在PyTorch* 2.5中为英特尔®数据中心GPU Max系列和英特尔®客户端GPU在Linux和Windows上准备就绪，这将英特尔GPU和SYCL*软件堆栈引入官方PyTorch堆栈，提供一致的用户体验，以拥抱更多的AI应用场景。

软件先决条件

访问 适用于Intel GPU的PyTorch安装先决条件 以获取更多关于以下内容的详细信息：

  1. Intel GPU驱动程序安装

  2. 英特尔支持包安装

  3. 环境设置

安装

二进制文件

平台 Linux

现在我们已经安装了所有必需的包并激活了环境。使用以下命令在Linux上安装pytorch，torchvision，torchaudio。

用于预览的轮子

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/test/xpu

对于夜间构建的轮子

pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu

平台 Windows

现在我们已经安装了所有必需的包并激活了环境。使用以下命令在Windows上安装pytorch，从源代码构建适用于torchvision和torchaudio的版本。

用于预览的轮子

pip3 install torch --index-url https://download.pytorch.org/whl/test/xpu

对于夜间构建的轮子

pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu

从源代码

从源代码构建适用于 torch，请参阅 PyTorch 安装从源代码构建。

从源代码构建适用于 torchvision，请参阅 Torchvision 安装从源代码构建。

从源代码构建适用于 torchaudio 的版本，请参阅 Torchaudio 安装 - 从源代码构建。

检查Intel GPU的可用性

要检查您的Intel GPU是否可用，您通常会使用以下代码：

import torch
torch.xpu.is_available()  # torch.xpu is the API for Intel GPU support

如果输出是 False，请检查以下步骤。

  1. Intel GPU驱动程序安装

  2. 英特尔支持包安装

  3. 环境设置

最小代码更改

如果您正在从cuda迁移代码，您需要将引用从cuda更改为xpu。例如：

# CUDA CODE
tensor = torch.tensor([1.0, 2.0]).to("cuda")

# CODE for Intel GPU
tensor = torch.tensor([1.0, 2.0]).to("xpu")

以下几点概述了PyTorch对Intel GPU的支持和限制：

  1. 支持训练和推理工作流程。

  2. 即时模式和torch.compile都受支持。

  3. 支持的数据类型包括FP32、BF16、FP16和自动混合精度（AMP）。

示例

本部分包含推理和训练工作流程的使用示例。

推理示例

这里有一些推理工作流程示例。

使用FP32进行推理

import torch
import torchvision.models as models

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

model = model.to("xpu")
data = data.to("xpu")

with torch.no_grad():
    model(data)

print("Execution finished")

使用AMP进行推理

import torch
import torchvision.models as models

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

model = model.to("xpu")
data = data.to("xpu")

with torch.no_grad():
    d = torch.rand(1, 3, 224, 224)
    d = d.to("xpu")
    # set dtype=torch.bfloat16 for BF16
    with torch.autocast(device_type="xpu", dtype=torch.float16, enabled=True):
        model(data)

print("Execution finished")

推理使用 torch.compile

import torch
import torchvision.models as models
import time

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
data = torch.rand(1, 3, 224, 224)
ITERS = 10

model = model.to("xpu")
data = data.to("xpu")

 for i in range(ITERS):
     start = time.time()
     with torch.no_grad():
         model(data)
         torch.xpu.synchronize()
     end = time.time()
     print(f"Inference time before torch.compile for iteration {i}: {(end-start)*1000} ms")

 model = torch.compile(model)
 for i in range(ITERS):
     start = time.time()
     with torch.no_grad():
         model(data)
         torch.xpu.synchronize()
     end = time.time()
     print(f"Inference time after torch.compile for iteration {i}: {(end-start)*1000} ms")

print("Execution finished")

训练示例

这里有一些训练流程示例。

使用FP32进行训练

import torch
import torchvision

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)
train_len = len(train_loader)

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

print(f"Initiating training")
for batch_idx, (data, target) in enumerate(train_loader):
    data = data.to("xpu")
    target = target.to("xpu")
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()
    if (batch_idx + 1) % 10 == 0:
         iteration_loss = loss.item()
         print(f"Iteration [{batch_idx+1}/{train_len}], Loss: {iteration_loss:.4f}")
torch.save(
    {
        "model_state_dict": model.state_dict(),
        "optimizer_state_dict": optimizer.state_dict(),
    },
    "checkpoint.pth",
)

print("Execution finished")

使用AMP进行训练

import torch
import torchvision

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

use_amp=True

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)
train_len = len(train_loader)

model = torchvision.models.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9)
scaler = torch.amp.GradScaler(enabled=use_amp)

model.train()
model = model.to("xpu")
criterion = criterion.to("xpu")

print(f"Initiating training")
for batch_idx, (data, target) in enumerate(train_loader):
    data = data.to("xpu")
    target = target.to("xpu")
    # set dtype=torch.bfloat16 for BF16
    with torch.autocast(device_type="xpu", dtype=torch.float16, enabled=use_amp):
        output = model(data)
        loss = criterion(output, target)
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()
    optimizer.zero_grad()
    if (batch_idx + 1) % 10 == 0:
         iteration_loss = loss.item()
         print(f"Iteration [{batch_idx+1}/{train_len}], Loss: {iteration_loss:.4f}")

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

print("Execution finished")

使用 torch.compile

import torch
import torchvision

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)
train_len = len(train_loader)

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

print(f"Initiating training with torch compile")
for batch_idx, (data, target) in enumerate(train_loader):
    data = data.to("xpu")
    target = target.to("xpu")
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()
    if (batch_idx + 1) % 10 == 0:
         iteration_loss = loss.item()
         print(f"Iteration [{batch_idx+1}/{train_len}], Loss: {iteration_loss:.4f}")
torch.save(
    {
        "model_state_dict": model.state_dict(),
        "optimizer_state_dict": optimizer.state_dict(),
    },
    "checkpoint.pth",
)

print("Execution finished")

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