train_cv_multi_gpu.py

import argparse
import math
import time
import traceback

import numpy as np
import torch as th
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import tqdm
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader

import dgl
import dgl.function as fn
import dgl.nn.pytorch as dglnn
from dgl.data import RedditDataset


class SAGEConvWithCV(nn.Module):
    def __init__(self, in_feats, out_feats, activation):
        super().__init__()
        self.W = nn.Linear(in_feats * 2, out_feats)
        self.activation = activation
        self.reset_parameters()

    def reset_parameters(self):
        gain = nn.init.calculate_gain("relu")
        nn.init.xavier_uniform_(self.W.weight, gain=gain)
        nn.init.constant_(self.W.bias, 0)

    def forward(self, block, H, HBar=None):
        if self.training:
            with block.local_scope():
                H_src, H_dst = H
                HBar_src, agg_HBar_dst = HBar
                block.dstdata["agg_hbar"] = agg_HBar_dst
                block.srcdata["hdelta"] = H_src - HBar_src
                block.update_all(
                    fn.copy_u("hdelta", "m"), fn.mean("m", "hdelta_new")
                )
                h_neigh = (
                    block.dstdata["agg_hbar"] + block.dstdata["hdelta_new"]
                )
                h = self.W(th.cat([H_dst, h_neigh], 1))
                if self.activation is not None:
                    h = self.activation(h)
                return h
        else:
            with block.local_scope():
                H_src, H_dst = H
                block.srcdata["h"] = H_src
                block.update_all(fn.copy_u("h", "m"), fn.mean("m", "h_new"))
                h_neigh = block.dstdata["h_new"]
                h = self.W(th.cat([H_dst, h_neigh], 1))
                if self.activation is not None:
                    h = self.activation(h)
                return h


class SAGE(nn.Module):
    def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation):
        super().__init__()
        self.n_layers = n_layers
        self.n_hidden = n_hidden
        self.n_classes = n_classes
        self.layers = nn.ModuleList()
        self.layers.append(SAGEConvWithCV(in_feats, n_hidden, activation))
        for i in range(1, n_layers - 1):
            self.layers.append(SAGEConvWithCV(n_hidden, n_hidden, activation))
        self.layers.append(SAGEConvWithCV(n_hidden, n_classes, None))

    def forward(self, blocks):
        h = blocks[0].srcdata["features"]
        updates = []
        for layer, block in zip(self.layers, blocks):
            # We need to first copy the representation of nodes on the RHS from the
            # appropriate nodes on the LHS.
            # Note that the shape of h is (num_nodes_LHS, D) and the shape of h_dst
            # would be (num_nodes_RHS, D)
            h_dst = h[: block.number_of_dst_nodes()]
            hbar_src = block.srcdata["hist"]
            agg_hbar_dst = block.dstdata["agg_hist"]
            # Then we compute the updated representation on the RHS.
            # The shape of h now becomes (num_nodes_RHS, D)
            h = layer(block, (h, h_dst), (hbar_src, agg_hbar_dst))
            block.dstdata["h_new"] = h
        return h

    def inference(self, g, x, batch_size, device):
        """
        Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).
        g : the entire graph.
        x : the input of entire node set.

        The inference code is written in a fashion that it could handle any number of nodes and
        layers.
        """
        # During inference with sampling, multi-layer blocks are very inefficient because
        # lots of computations in the first few layers are repeated.
        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
        # on each layer are of course splitted in batches.
        # TODO: can we standardize this?
        nodes = th.arange(g.number_of_nodes())
        for l, layer in enumerate(self.layers):
            y = g.ndata["hist_%d" % (l + 1)]

            for start in tqdm.trange(0, len(nodes), batch_size):
                end = start + batch_size
                batch_nodes = nodes[start:end]
                block = dgl.to_block(
                    dgl.in_subgraph(g, batch_nodes), batch_nodes
                )
                induced_nodes = block.srcdata[dgl.NID]

                h = x[induced_nodes].to(device)
                block = block.to(device)
                h_dst = h[: block.number_of_dst_nodes()]
                h = layer(block, (h, h_dst))

                y[start:end] = h.cpu()

            x = y
        return y


class NeighborSampler(object):
    def __init__(self, g, fanouts):
        self.g = g
        self.fanouts = fanouts

    def sample_blocks(self, seeds):
        seeds = th.LongTensor(seeds)
        blocks = []
        hist_blocks = []
        for fanout in self.fanouts:
            # For each seed node, sample ``fanout`` neighbors.
            frontier = dgl.sampling.sample_neighbors(self.g, seeds, fanout)
            # For history aggregation we sample all neighbors.
            hist_frontier = dgl.in_subgraph(self.g, seeds)
            # Then we compact the frontier into a bipartite graph for message passing.
            block = dgl.to_block(frontier, seeds)
            hist_block = dgl.to_block(hist_frontier, seeds)
            # Obtain the seed nodes for next layer.
            seeds = block.srcdata[dgl.NID]

            blocks.insert(0, block)
            hist_blocks.insert(0, hist_block)
        return blocks, hist_blocks


def compute_acc(pred, labels):
    """
    Compute the accuracy of prediction given the labels.
    """
    return (th.argmax(pred, dim=1) == labels).float().sum() / len(pred)


def evaluate(model, g, labels, val_mask, batch_size, device):
    """
    Evaluate the model on the validation set specified by ``val_mask``.
    g : The entire graph.
    inputs : The features of all the nodes.
    labels : The labels of all the nodes.
    val_mask : A 0-1 mask indicating which nodes do we actually compute the accuracy for.
    batch_size : Number of nodes to compute at the same time.
    device : The GPU device to evaluate on.
    """
    model.eval()
    with th.no_grad():
        inputs = g.ndata["features"]
        pred = model.inference(
            g, inputs, batch_size, device
        )  # also recomputes history tensors
    model.train()
    return compute_acc(pred[val_mask], labels[val_mask])


def load_subtensor(
    g, labels, blocks, hist_blocks, dev_id, aggregation_on_device=False
):
    """
    Copys features and labels of a set of nodes onto GPU.
    """
    blocks[0].srcdata["features"] = g.ndata["features"][
        blocks[0].srcdata[dgl.NID]
    ]
    blocks[-1].dstdata["label"] = labels[blocks[-1].dstdata[dgl.NID]]
    ret_blocks = []
    ret_hist_blocks = []
    for i, (block, hist_block) in enumerate(zip(blocks, hist_blocks)):
        hist_col = "features" if i == 0 else "hist_%d" % i
        block.srcdata["hist"] = g.ndata[hist_col][block.srcdata[dgl.NID]]

        # Aggregate history
        hist_block.srcdata["hist"] = g.ndata[hist_col][
            hist_block.srcdata[dgl.NID]
        ]
        if aggregation_on_device:
            hist_block = hist_block.to(dev_id)
            hist_block.srcdata["hist"] = hist_block.srcdata["hist"]
        hist_block.update_all(fn.copy_u("hist", "m"), fn.mean("m", "agg_hist"))

        block = block.to(dev_id)
        if not aggregation_on_device:
            hist_block = hist_block.to(dev_id)
        block.dstdata["agg_hist"] = hist_block.dstdata["agg_hist"]
        ret_blocks.append(block)
        ret_hist_blocks.append(hist_block)
    return ret_blocks, ret_hist_blocks


def create_history_storage(g, args, n_classes):
    # Initialize history storage
    for l in range(args.num_layers):
        dim = args.num_hidden if l != args.num_layers - 1 else n_classes
        g.ndata["hist_%d" % (l + 1)] = th.zeros(
            g.number_of_nodes(), dim
        ).share_memory_()


def init_history(g, model, dev_id, batch_size):
    with th.no_grad():
        model.inference(
            g, g.ndata["features"], batch_size, dev_id
        )  # replaces hist_i features in-place


def update_history(g, blocks):
    with th.no_grad():
        for i, block in enumerate(blocks):
            ids = block.dstdata[dgl.NID].cpu()
            hist_col = "hist_%d" % (i + 1)

            h_new = block.dstdata["h_new"].cpu()
            g.ndata[hist_col][ids] = h_new


def run(proc_id, n_gpus, args, devices, data):
    dropout = 0.2

    dev_id = devices[proc_id]
    if n_gpus > 1:
        dist_init_method = "tcp://{master_ip}:{master_port}".format(
            master_ip="127.0.0.1", master_port="12345"
        )
        world_size = n_gpus
        th.distributed.init_process_group(
            backend="nccl",
            init_method=dist_init_method,
            world_size=world_size,
            rank=proc_id,
        )
    th.cuda.set_device(dev_id)

    # Unpack data
    train_mask, val_mask, in_feats, labels, n_classes, g = data
    train_nid = train_mask.nonzero().squeeze()
    val_nid = val_mask.nonzero().squeeze()

    # Create sampler
    sampler = NeighborSampler(g, [int(_) for _ in args.fan_out.split(",")])

    # Create PyTorch DataLoader for constructing blocks
    if n_gpus > 1:
        dist_sampler = th.utils.data.distributed.DistributedSampler(
            train_nid.numpy(), shuffle=True, drop_last=False
        )
        dataloader = DataLoader(
            dataset=train_nid.numpy(),
            batch_size=args.batch_size,
            collate_fn=sampler.sample_blocks,
            sampler=dist_sampler,
            num_workers=args.num_workers_per_gpu,
        )
    else:
        dataloader = DataLoader(
            dataset=train_nid.numpy(),
            batch_size=args.batch_size,
            collate_fn=sampler.sample_blocks,
            shuffle=True,
            drop_last=False,
            num_workers=args.num_workers_per_gpu,
        )

    # Define model
    model = SAGE(in_feats, args.num_hidden, n_classes, args.num_layers, F.relu)

    # Move the model to GPU and define optimizer
    model = model.to(dev_id)
    if n_gpus > 1:
        model = DistributedDataParallel(
            model, device_ids=[dev_id], output_device=dev_id
        )
    loss_fcn = nn.CrossEntropyLoss()
    loss_fcn = loss_fcn.to(dev_id)
    optimizer = optim.Adam(model.parameters(), lr=args.lr)

    # Compute history tensor and their aggregation before training on CPU
    model.eval()
    if n_gpus > 1:
        if proc_id == 0:
            init_history(g, model.module, dev_id, args.val_batch_size)
        th.distributed.barrier()
    else:
        init_history(g, model, dev_id, args.val_batch_size)
    model.train()

    # Training loop
    avg = 0
    iter_tput = []
    for epoch in range(args.num_epochs):
        if n_gpus > 1:
            dist_sampler.set_epoch(epoch)
        tic = time.time()
        model.train()
        for step, (blocks, hist_blocks) in enumerate(dataloader):
            if proc_id == 0:
                tic_step = time.time()

            # The nodes for input lies at the LHS side of the first block.
            # The nodes for output lies at the RHS side of the last block.
            seeds = blocks[-1].dstdata[dgl.NID]

            blocks, hist_blocks = load_subtensor(
                g, labels, blocks, hist_blocks, dev_id, True
            )

            # forward
            batch_pred = model(blocks)
            # update history
            update_history(g, blocks)
            # compute loss
            batch_labels = blocks[-1].dstdata["label"]
            loss = loss_fcn(batch_pred, batch_labels)
            # backward
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            if proc_id == 0:
                iter_tput.append(len(seeds) * n_gpus / (time.time() - tic_step))
            if step % args.log_every == 0 and proc_id == 0:
                acc = compute_acc(batch_pred, batch_labels)
                print(
                    "Epoch {:05d} | Step {:05d} | Loss {:.4f} | Train Acc {:.4f} | Speed (samples/sec) {:.4f}".format(
                        epoch,
                        step,
                        loss.item(),
                        acc.item(),
                        np.mean(iter_tput[3:]),
                    )
                )

        if n_gpus > 1:
            th.distributed.barrier()

        toc = time.time()
        if proc_id == 0:
            print("Epoch Time(s): {:.4f}".format(toc - tic))
            if epoch >= 5:
                avg += toc - tic
            if epoch % args.eval_every == 0 and epoch != 0:
                model.eval()
                eval_acc = evaluate(
                    model if n_gpus == 1 else model.module,
                    g,
                    labels,
                    val_nid,
                    args.val_batch_size,
                    dev_id,
                )
                print("Eval Acc {:.4f}".format(eval_acc))

    if n_gpus > 1:
        th.distributed.barrier()
    if proc_id == 0:
        print("Avg epoch time: {}".format(avg / (epoch - 4)))


if __name__ == "__main__":
    argparser = argparse.ArgumentParser("multi-gpu training")
    argparser.add_argument("--gpu", type=str, default="0")
    argparser.add_argument("--num-epochs", type=int, default=20)
    argparser.add_argument("--num-hidden", type=int, default=16)
    argparser.add_argument("--num-layers", type=int, default=2)
    argparser.add_argument("--fan-out", type=str, default="1,1")
    argparser.add_argument("--batch-size", type=int, default=1000)
    argparser.add_argument("--val-batch-size", type=int, default=1000)
    argparser.add_argument("--log-every", type=int, default=20)
    argparser.add_argument("--eval-every", type=int, default=5)
    argparser.add_argument("--lr", type=float, default=0.003)
    argparser.add_argument("--num-workers-per-gpu", type=int, default=0)
    args = argparser.parse_args()

    devices = list(map(int, args.gpu.split(",")))
    n_gpus = len(devices)

    # load reddit data
    data = RedditDataset(self_loop=True)
    n_classes = data.num_classes
    g = data[0]
    features = g.ndata["feat"]
    in_feats = features.shape[1]
    labels = g.ndata["label"]
    train_mask = g.ndata["train_mask"]
    val_mask = g.ndata["val_mask"]
    g.ndata["features"] = features.share_memory_()
    create_history_storage(g, args, n_classes)

    # Create csr/coo/csc formats before launching training processes with multi-gpu.
    # This avoids creating certain formats in each sub-process, which saves momory and CPU.
    g.create_formats_()
    # Pack data
    data = train_mask, val_mask, in_feats, labels, n_classes, g

    if n_gpus == 1:
        run(0, n_gpus, args, devices, data)
    else:
        mp.spawn(run, args=(n_gpus, args, devices, data), nprocs=n_gpus)