[Model] Pinsage example that uses sparse embedding update (dmlc#1676)

* pinsage example that uses sparse embedding update

* add difference from paper

examples/pytorch/pinsage/README.md

@@ -28,4 +28,35 @@ item embeddings, which are learned as outputs of PinSAGE.
 python model.py data.pkl --num-epochs 300 --num-workers 2 --device cuda:0 --hidden-dims 64
 ```
 
-The HITS@10 is 0.01241, compared to 0.01220 with SLIM with the same dimensionality.
+The implementation here also assigns a learnable vector to each item.  If your hidden
+state size is so large that the learnable vectors cannot fit into GPU, use this script
+for sparse embedding update (written with `torch.optim.SparseAdam`) instead:
+
+
+```
+python model_sparse.py data.pkl --num-epochs 300 --num-workers 2 --device cuda:0 --hidden-dims 1024
+```
+
+Note that since the embedding update is done on CPU, it will be significantly slower than doing
+everything on GPU.
+
+The HITS@10 is 0.01241, compared to 0.01220 with SLIM with the same dimensionality.\
+
+## Difference from the paper
+
+The implementation here is different from what being described in the paper:
+
+1. The paper described a supervised setting where the authors have a ground truth set of which items are
+   relevant.  However, in traditional recommender system datasets we don't have such labels other than
+   which items are interacted by which users (as well as the user/item's own features).  Therefore, I
+   adapted PinSAGE to an unsupervised setting where I predict whether two items are cointeracted by the
+   same user.
+2. PinSAGE paper explicitly stated that the items do not learnable embeddings of nodes, but directly
+   express the embeddings as a function of node features.  While this is reasonable for rich datasets like
+   Pinterest's where images and texts are rich enough to distinguish the items from each other, it is
+   unfortunately not the case for traditional recommender system datasets like MovieLens or Nowplaying-RS
+   where we only have a bunch of categorical or numeric variables.  I found adding a learnable embedding
+   for each item still helpful for those datasets.
+3. The PinSAGE paper directly pass the GNN output to an MLP and make the result the final item
+   representation.  Here, I'm adding the GNN output with the node's own learnable embedding as
+   the final item representation instead.

examples/pytorch/pinsage/model_sparse.py

@@ -0,0 +1,147 @@
+import pickle
+import argparse
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import torchtext
+import dgl
+import tqdm
+
+import layers
+import sampler as sampler_module
+import evaluation
+
+class PinSAGEModel(nn.Module):
+    def __init__(self, full_graph, ntype, textsets, hidden_dims, n_layers):
+        super().__init__()
+
+        self.proj = layers.LinearProjector(full_graph, ntype, textsets, hidden_dims)
+        self.sage = layers.SAGENet(hidden_dims, n_layers)
+        self.scorer = layers.ItemToItemScorer(full_graph, ntype)
+
+    def forward(self, pos_graph, neg_graph, blocks, item_emb):
+        h_item = self.get_repr(blocks, item_emb)
+        pos_score = self.scorer(pos_graph, h_item)
+        neg_score = self.scorer(neg_graph, h_item)
+        return (neg_score - pos_score + 1).clamp(min=0)
+
+    def get_repr(self, blocks, item_emb):
+        # project features
+        h_item = self.proj(blocks[0].srcdata)
+        h_item_dst = self.proj(blocks[-1].dstdata)
+
+        # add to the item embedding itself
+        h_item = h_item + item_emb(blocks[0].srcdata[dgl.NID].cpu()).to(h_item)
+        h_item_dst = h_item_dst + item_emb(blocks[-1].dstdata[dgl.NID].cpu()).to(h_item_dst)
+
+        return h_item_dst + self.sage(blocks, h_item)
+
+def train(dataset, args):
+    g = dataset['train-graph']
+    val_matrix = dataset['val-matrix'].tocsr()
+    test_matrix = dataset['test-matrix'].tocsr()
+    item_texts = dataset['item-texts']
+    user_ntype = dataset['user-type']
+    item_ntype = dataset['item-type']
+    user_to_item_etype = dataset['user-to-item-type']
+    timestamp = dataset['timestamp-edge-column']
+
+    device = torch.device(args.device)
+
+    # Prepare torchtext dataset and vocabulary
+    fields = {}
+    examples = []
+    for key, texts in item_texts.items():
+        fields[key] = torchtext.data.Field(include_lengths=True, lower=True, batch_first=True)
+    for i in range(g.number_of_nodes(item_ntype)):
+        example = torchtext.data.Example.fromlist(
+            [item_texts[key][i] for key in item_texts.keys()],
+            [(key, fields[key]) for key in item_texts.keys()])
+        examples.append(example)
+    textset = torchtext.data.Dataset(examples, fields)
+    for key, field in fields.items():
+        field.build_vocab(getattr(textset, key))
+        #field.build_vocab(getattr(textset, key), vectors='fasttext.simple.300d')
+
+    # Sampler
+    batch_sampler = sampler_module.ItemToItemBatchSampler(
+        g, user_ntype, item_ntype, args.batch_size)
+    neighbor_sampler = sampler_module.NeighborSampler(
+        g, user_ntype, item_ntype, args.random_walk_length,
+        args.random_walk_restart_prob, args.num_random_walks, args.num_neighbors,
+        args.num_layers)
+    collator = sampler_module.PinSAGECollator(neighbor_sampler, g, item_ntype, textset)
+    dataloader = DataLoader(
+        batch_sampler,
+        collate_fn=collator.collate_train,
+        num_workers=args.num_workers)
+    dataloader_test = DataLoader(
+        torch.arange(g.number_of_nodes(item_ntype)),
+        batch_size=args.batch_size,
+        collate_fn=collator.collate_test,
+        num_workers=args.num_workers)
+    dataloader_it = iter(dataloader)
+
+    # Model
+    model = PinSAGEModel(g, item_ntype, textset, args.hidden_dims, args.num_layers).to(device)
+    item_emb = nn.Embedding(g.number_of_nodes(item_ntype), args.hidden_dims, sparse=True)
+    # Optimizer
+    opt = torch.optim.Adam(model.parameters(), lr=args.lr)
+    opt_emb = torch.optim.SparseAdam(item_emb.parameters(), lr=args.lr)
+
+    # For each batch of head-tail-negative triplets...
+    for epoch_id in range(args.num_epochs):
+        model.train()
+        for batch_id in tqdm.trange(args.batches_per_epoch):
+            pos_graph, neg_graph, blocks = next(dataloader_it)
+            # Copy to GPU
+            for i in range(len(blocks)):
+                blocks[i] = blocks[i].to(device)
+            pos_graph = pos_graph.to(device)
+            neg_graph = neg_graph.to(device)
+
+            loss = model(pos_graph, neg_graph, blocks, item_emb).mean()
+            opt.zero_grad()
+            opt_emb.zero_grad()
+            loss.backward()
+            opt.step()
+            opt_emb.step()
+
+        # Evaluate
+        model.eval()
+        with torch.no_grad():
+            item_batches = torch.arange(g.number_of_nodes(item_ntype)).split(args.batch_size)
+            h_item_batches = []
+            for blocks in tqdm.tqdm(dataloader_test):
+                for i in range(len(blocks)):
+                    blocks[i] = blocks[i].to(device)
+
+                h_item_batches.append(model.get_repr(blocks, item_emb))
+            h_item = torch.cat(h_item_batches, 0)
+
+            print(evaluation.evaluate_nn(dataset, h_item, args.k, args.batch_size))
+
+if __name__ == '__main__':
+    # Arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('dataset_path', type=str)
+    parser.add_argument('--random-walk-length', type=int, default=2)
+    parser.add_argument('--random-walk-restart-prob', type=float, default=0.5)
+    parser.add_argument('--num-random-walks', type=int, default=10)
+    parser.add_argument('--num-neighbors', type=int, default=3)
+    parser.add_argument('--num-layers', type=int, default=2)
+    parser.add_argument('--hidden-dims', type=int, default=16)
+    parser.add_argument('--batch-size', type=int, default=32)
+    parser.add_argument('--device', type=str, default='cpu')        # can also be "cuda:0"
+    parser.add_argument('--num-epochs', type=int, default=1)
+    parser.add_argument('--batches-per-epoch', type=int, default=20000)
+    parser.add_argument('--num-workers', type=int, default=0)
+    parser.add_argument('--lr', type=float, default=3e-5)
+    parser.add_argument('-k', type=int, default=10)
+    args = parser.parse_args()
+
+    # Load dataset
+    with open(args.dataset_path, 'rb') as f:
+        dataset = pickle.load(f)
+    train(dataset, args)