快速开始

备注

在本示例之前，请确保已经安装了 DeepSpeed 环境。 如果还未安装，可以执行 pip install deepspeed 完成安装。

1. 使用DeepSpeed多卡并行训练

以下代码使用了cifar10数据集，使用DeepSpeed训练模型在多张NPU卡上进行模型训练（来自 DeepSpeed Examples），自DeepSpeed v0.12.6之后，代码无需任何修改，即可自动检测NPU并进行训练。

import argparse

import deepspeed
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from deepspeed.accelerator import get_accelerator
from deepspeed.moe.utils import split_params_into_different_moe_groups_for_optimizer


def add_argument():
    parser = argparse.ArgumentParser(description="CIFAR")

    # For train.
    parser.add_argument(
        "-e",
        "--epochs",
        default=30,
        type=int,
        help="number of total epochs (default: 30)",
    )
    parser.add_argument(
        "--local_rank",
        type=int,
        default=-1,
        help="local rank passed from distributed launcher",
    )
    parser.add_argument(
        "--log-interval",
        type=int,
        default=2000,
        help="output logging information at a given interval",
    )

    # For mixed precision training.
    parser.add_argument(
        "--dtype",
        default="fp16",
        type=str,
        choices=["bf16", "fp16", "fp32"],
        help="Datatype used for training",
    )

    # For ZeRO Optimization.
    parser.add_argument(
        "--stage",
        default=0,
        type=int,
        choices=[0, 1, 2, 3],
        help="Datatype used for training",
    )

    # For MoE (Mixture of Experts).
    parser.add_argument(
        "--moe",
        default=False,
        action="store_true",
        help="use deepspeed mixture of experts (moe)",
    )
    parser.add_argument(
        "--ep-world-size", default=1, type=int, help="(moe) expert parallel world size"
    )
    parser.add_argument(
        "--num-experts",
        type=int,
        nargs="+",
        default=[
            1,
        ],
        help="number of experts list, MoE related.",
    )
    parser.add_argument(
        "--mlp-type",
        type=str,
        default="standard",
        help="Only applicable when num-experts > 1, accepts [standard, residual]",
    )
    parser.add_argument(
        "--top-k", default=1, type=int, help="(moe) gating top 1 and 2 supported"
    )
    parser.add_argument(
        "--min-capacity",
        default=0,
        type=int,
        help="(moe) minimum capacity of an expert regardless of the capacity_factor",
    )
    parser.add_argument(
        "--noisy-gate-policy",
        default=None,
        type=str,
        help="(moe) noisy gating (only supported with top-1). Valid values are None, RSample, and Jitter",
    )
    parser.add_argument(
        "--moe-param-group",
        default=False,
        action="store_true",
        help="(moe) create separate moe param groups, required when using ZeRO w. MoE",
    )

    # Include DeepSpeed configuration arguments.
    parser = deepspeed.add_config_arguments(parser)

    args = parser.parse_args()

    return args


def create_moe_param_groups(model):
    """Create separate parameter groups for each expert."""
    parameters = {"params": [p for p in model.parameters()], "name": "parameters"}
    return split_params_into_different_moe_groups_for_optimizer(parameters)


def get_ds_config(args):
    """Get the DeepSpeed configuration dictionary."""
    ds_config = {
        "train_batch_size": 16,
        "steps_per_print": 2000,
        "optimizer": {
            "type": "Adam",
            "params": {
                "lr": 0.001,
                "betas": [0.8, 0.999],
                "eps": 1e-8,
                "weight_decay": 3e-7,
            },
        },
        "scheduler": {
            "type": "WarmupLR",
            "params": {
                "warmup_min_lr": 0,
                "warmup_max_lr": 0.001,
                "warmup_num_steps": 1000,
            },
        },
        "gradient_clipping": 1.0,
        "prescale_gradients": False,
        "bf16": {"enabled": args.dtype == "bf16"},
        "fp16": {
            "enabled": args.dtype == "fp16",
            "fp16_master_weights_and_grads": False,
            "loss_scale": 0,
            "loss_scale_window": 500,
            "hysteresis": 2,
            "min_loss_scale": 1,
            "initial_scale_power": 15,
        },
        "wall_clock_breakdown": False,
        "zero_optimization": {
            "stage": args.stage,
            "allgather_partitions": True,
            "reduce_scatter": True,
            "allgather_bucket_size": 50000000,
            "reduce_bucket_size": 50000000,
            "overlap_comm": True,
            "contiguous_gradients": True,
            "cpu_offload": False,
        },
    }
    return ds_config


class Net(nn.Module):
    def __init__(self, args):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.moe = args.moe
        if self.moe:
            fc3 = nn.Linear(84, 84)
            self.moe_layer_list = []
            for n_e in args.num_experts:
                # Create moe layers based on the number of experts.
                self.moe_layer_list.append(
                    deepspeed.moe.layer.MoE(
                        hidden_size=84,
                        expert=fc3,
                        num_experts=n_e,
                        ep_size=args.ep_world_size,
                        use_residual=args.mlp_type == "residual",
                        k=args.top_k,
                        min_capacity=args.min_capacity,
                        noisy_gate_policy=args.noisy_gate_policy,
                    )
                )
            self.moe_layer_list = nn.ModuleList(self.moe_layer_list)
            self.fc4 = nn.Linear(84, 10)
        else:
            self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        if self.moe:
            for layer in self.moe_layer_list:
                x, _, _ = layer(x)
            x = self.fc4(x)
        else:
            x = self.fc3(x)
        return x


def test(model_engine, testset, local_device, target_dtype, test_batch_size=4):
    """Test the network on the test data.

    Args:
        model_engine (deepspeed.runtime.engine.DeepSpeedEngine): the DeepSpeed engine.
        testset (torch.utils.data.Dataset): the test dataset.
        local_device (str): the local device name.
        target_dtype (torch.dtype): the target datatype for the test data.
        test_batch_size (int): the test batch size.

    """
    # The 10 classes for CIFAR10.
    classes = (
        "plane",
        "car",
        "bird",
        "cat",
        "deer",
        "dog",
        "frog",
        "horse",
        "ship",
        "truck",
    )

    # Define the test dataloader.
    testloader = torch.utils.data.DataLoader(
        testset, batch_size=test_batch_size, shuffle=False, num_workers=0
    )

    # For total accuracy.
    correct, total = 0, 0
    # For accuracy per class.
    class_correct = list(0.0 for i in range(10))
    class_total = list(0.0 for i in range(10))

    # Start testing.
    model_engine.eval()
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            if target_dtype != None:
                images = images.to(target_dtype)
            outputs = model_engine(images.to(local_device))
            _, predicted = torch.max(outputs.data, 1)
            # Count the total accuracy.
            total += labels.size(0)
            correct += (predicted == labels.to(local_device)).sum().item()

            # Count the accuracy per class.
            batch_correct = (predicted == labels.to(local_device)).squeeze()
            for i in range(test_batch_size):
                label = labels[i]
                class_correct[label] += batch_correct[i].item()
                class_total[label] += 1

    if model_engine.local_rank == 0:
        print(
            f"Accuracy of the network on the {total} test images: {100 * correct / total : .0f} %"
        )

        # For all classes, print the accuracy.
        for i in range(10):
            print(
                f"Accuracy of {classes[i] : >5s} : {100 * class_correct[i] / class_total[i] : 2.0f} %"
            )


def main(args):
    # Initialize DeepSpeed distributed backend.
    deepspeed.init_distributed()

    ########################################################################
    # Step1. Data Preparation.
    #
    # The output of torchvision datasets are PILImage images of range [0, 1].
    # We transform them to Tensors of normalized range [-1, 1].
    #
    # Note:
    #     If running on Windows and you get a BrokenPipeError, try setting
    #     the num_worker of torch.utils.data.DataLoader() to 0.
    ########################################################################
    transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    )

    if torch.distributed.get_rank() != 0:
        # Might be downloading cifar data, let rank 0 download first.
        torch.distributed.barrier()

    # Load or download cifar data.
    trainset = torchvision.datasets.CIFAR10(
        root="./data", train=True, download=True, transform=transform
    )
    testset = torchvision.datasets.CIFAR10(
        root="./data", train=False, download=True, transform=transform
    )

    if torch.distributed.get_rank() == 0:
        # Cifar data is downloaded, indicate other ranks can proceed.
        torch.distributed.barrier()

    ########################################################################
    # Step 2. Define the network with DeepSpeed.
    #
    # First, we define a Convolution Neural Network.
    # Then, we define the DeepSpeed configuration dictionary and use it to
    # initialize the DeepSpeed engine.
    ########################################################################
    net = Net(args)

    # Get list of parameters that require gradients.
    parameters = filter(lambda p: p.requires_grad, net.parameters())

    # If using MoE, create separate param groups for each expert.
    if args.moe_param_group:
        parameters = create_moe_param_groups(net)

    # Initialize DeepSpeed to use the following features.
    #   1) Distributed model.
    #   2) Distributed data loader.
    #   3) DeepSpeed optimizer.
    ds_config = get_ds_config(args)
    model_engine, optimizer, trainloader, __ = deepspeed.initialize(
        args=args,
        model=net,
        model_parameters=parameters,
        training_data=trainset,
        config=ds_config,
    )

    # Get the local device name (str) and local rank (int).
    local_device = get_accelerator().device_name(model_engine.local_rank)
    local_rank = model_engine.local_rank

    # For float32, target_dtype will be None so no datatype conversion needed.
    target_dtype = None
    if model_engine.bfloat16_enabled():
        target_dtype = torch.bfloat16
    elif model_engine.fp16_enabled():
        target_dtype = torch.half

    # Define the Classification Cross-Entropy loss function.
    criterion = nn.CrossEntropyLoss()

    ########################################################################
    # Step 3. Train the network.
    #
    # This is when things start to get interesting.
    # We simply have to loop over our data iterator, and feed the inputs to the
    # network and optimize. (DeepSpeed handles the distributed details for us!)
    ########################################################################

    for epoch in range(args.epochs):  # loop over the dataset multiple times
        running_loss = 0.0
        for i, data in enumerate(trainloader):
            # Get the inputs. ``data`` is a list of [inputs, labels].
            inputs, labels = data[0].to(local_device), data[1].to(local_device)

            # Try to convert to target_dtype if needed.
            if target_dtype != None:
                inputs = inputs.to(target_dtype)

            outputs = model_engine(inputs)
            loss = criterion(outputs, labels)

            model_engine.backward(loss)
            model_engine.step()

            # Print statistics
            running_loss += loss.item()
            if local_rank == 0 and i % args.log_interval == (
                args.log_interval - 1
            ):  # Print every log_interval mini-batches.
                print(
                    f"[{epoch + 1 : d}, {i + 1 : 5d}] loss: {running_loss / args.log_interval : .3f}"
                )
                running_loss = 0.0
    print("Finished Training")

    ########################################################################
    # Step 4. Test the network on the test data.
    ########################################################################
    test(model_engine, testset, local_device, target_dtype)


if __name__ == "__main__":
    args = add_argument()
    main(args)

2. 训练结果查看

训练完成后，会打印模型对图像识别的结果。

Finished Training
Accuracy of the network on the 10000 test images:  57 %
Accuracy of plane :  65 %
Accuracy of   car :  67 %
Accuracy of  bird :  52 %
Accuracy of   cat :  34 %
Accuracy of  deer :  52 %
Accuracy of   dog :  49 %
Accuracy of  frog :  59 %
Accuracy of horse :  66 %
Accuracy of  ship :  66 %
Accuracy of truck :  56 %