RGAT baseline for OGB-LSC node classification challenge (dmlc#2810)

* RGAT baseline

* update links (data.dgl.ai hasn't refreshed yet)

* nvm still use dgl.ai domain

* descriptions

* actually it took less

* address comments

* stop worrying about cache

* update links

* oops

examples/pytorch/ogb_lsc/MAG240M/README.md

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+# Baseline Code for MAG240M
+
+The code is ported from the R-GAT examples [here](https://github.com/snap-stanford/ogb/tree/master/examples/lsc/mag240m). Please refer to the [OGB-LSC paper](https://arxiv.org/abs/2103.09430) for the detailed setting.
+
+## Installation Requirements
+
+```
+ogb>=1.3.0
+torch>=1.7.0
+```
+
+## Running Preprocessing Script
+
+```
+python preprocess.py \
+    --rootdir . \
+    --author-output-path ./author.npy \
+    --inst-output-path ./inst.npy \
+    --graph-output-path ./graph.dgl \
+    --graph-as-homogeneous \
+    --full-output-path ./full.npy
+```
+
+This will give you the following files:
+
+* `author.npy`: The author features, preprocessed by averaging the neighboring paper features.
+* `inst.npy`: The institution features, preprocessed by averaging the neighboring author features.
+* `graph.dgl`: The *homogenized* DGL graph stored in CSC format, which is friendly for neighbor sampling.
+  Edge types are stored on the edges as an `int8` feature.  Nodes are in the order of author, institution,
+  and paper.
+* `full.npy`: The concatenated author, institution, and paper features.
+
+Since that will usually take a long time, we also offer the above files for download:
+
+* [`author.npy`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/author.npy)
+* [`inst.npy`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/inst.npy)
+* [`graph.dgl`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/graph.dgl)
+* [`full.npy`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/full.npy)
+
+In addition, we offer
+
+* [`full_feat.npy`](https://dgl-data.s3-accelerate.amazonaws.com/dataset/OGB-LSC/full_feat.npy): The preprocessed full feature matrix
+  for running OGB's own baseline. Note that the features are concatenated in the order of paper, author, and
+  institution, unlike the one in our baseline code.  It is also preprocessed in float32 arithmetics instead
+  of float16 arithmetics.
+
+## Running Training Script
+
+```
+python train.py \
+    --rootdir . \
+    --graph-preprocess-path ./graph.dgl \
+    --full-preprocess-path ./full.npy
+```
+
+The validation accuracy is 0.701.  We do not have ground truth test labels so we do not report
+test accuracy.
+
+## Hardware configurations
+
+We successfully run 8 experiments in parallel on an AWS p4d.24x large instance with the preprocessed feature
+matrices stored on an NVMe SSD to enable fast disk read.  Each experiment requires less than 128GB CPU
+memory and less than 12GB GPU memory to run.  Every epoch takes around 6 minutes 30 seconds to train and
+1 minutes 40 seconds to validate.
+
+If your hard drive is slow, it is best to load all the features into memory for a reasonable training speed.
+The CPU memory consumption will go up to as large as 512GB though.

examples/pytorch/ogb_lsc/MAG240M/preprocess.py

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+import ogb
+from ogb.lsc import MAG240MDataset
+import tqdm
+import numpy as np
+import torch
+import dgl
+import dgl.function as fn
+import argparse
+import os
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--rootdir', type=str, default='.', help='Directory to download the OGB dataset.')
+parser.add_argument('--author-output-path', type=str, help='Path to store the author features.')
+parser.add_argument('--inst-output-path', type=str,
+                    help='Path to store the institution features.')
+parser.add_argument('--graph-output-path', type=str, help='Path to store the graph.')
+parser.add_argument('--graph-format', type=str, default='csc', help='Graph format (coo, csr or csc).')
+parser.add_argument('--graph-as-homogeneous', action='store_true', help='Store the graph as DGL homogeneous graph.')
+parser.add_argument('--full-output-path', type=str,
+                    help='Path to store features of all nodes.  Effective only when graph is homogeneous.')
+args = parser.parse_args()
+
+print('Building graph')
+dataset = MAG240MDataset(root=args.rootdir)
+ei_writes = dataset.edge_index('author', 'writes', 'paper')
+ei_cites = dataset.edge_index('paper', 'paper')
+ei_affiliated = dataset.edge_index('author', 'institution')
+
+# We sort the nodes starting with the papers, then the authors, then the institutions.
+author_offset = 0
+inst_offset = author_offset + dataset.num_authors
+paper_offset = inst_offset + dataset.num_institutions
+
+g = dgl.heterograph({
+    ('author', 'write', 'paper'): (ei_writes[0], ei_writes[1]),
+    ('paper', 'write-by', 'author'): (ei_writes[1], ei_writes[0]),
+    ('author', 'affiliate-with', 'institution'): (ei_affiliated[0], ei_affiliated[1]),
+    ('institution', 'affiliate', 'author'): (ei_affiliated[1], ei_affiliated[0]),
+    ('paper', 'cite', 'paper'): (np.concatenate([ei_cites[0], ei_cites[1]]), np.concatenate([ei_cites[1], ei_cites[0]]))
+    })
+
+paper_feat = dataset.paper_feat
+author_feat = np.memmap(args.author_output_path, mode='w+', dtype='float16', shape=(dataset.num_authors, dataset.num_paper_features))
+inst_feat = np.memmap(args.inst_output_path, mode='w+', dtype='float16', shape=(dataset.num_institutions, dataset.num_paper_features))
+
+# Iteratively process author features along the feature dimension.
+BLOCK_COLS = 16
+with tqdm.trange(0, dataset.num_paper_features, BLOCK_COLS) as tq:
+    for start in tq:
+        tq.set_postfix_str('Reading paper features...')
+        g.nodes['paper'].data['x'] = torch.FloatTensor(paper_feat[:, start:start + BLOCK_COLS].astype('float32'))
+        # Compute author features...
+        tq.set_postfix_str('Computing author features...')
+        g.update_all(fn.copy_u('x', 'm'), fn.mean('m', 'x'), etype='write-by')
+        # Then institution features...
+        tq.set_postfix_str('Computing institution features...')
+        g.update_all(fn.copy_u('x', 'm'), fn.mean('m', 'x'), etype='affiliate-with')
+        tq.set_postfix_str('Writing author features...')
+        author_feat[:, start:start + BLOCK_COLS] = g.nodes['author'].data['x'].numpy().astype('float16')
+        tq.set_postfix_str('Writing institution features...')
+        inst_feat[:, start:start + BLOCK_COLS] = g.nodes['institution'].data['x'].numpy().astype('float16')
+        del g.nodes['paper'].data['x']
+        del g.nodes['author'].data['x']
+        del g.nodes['institution'].data['x']
+author_feat.flush()
+inst_feat.flush()
+
+# Convert to homogeneous if needed.  (The RGAT baseline needs homogeneous graph)
+if args.graph_as_homogeneous:
+    # Process graph
+    g = dgl.to_homogeneous(g)
+    # DGL ensures that nodes with the same type are put together with the order preserved.
+    # DGL also ensures that the node types are sorted in ascending order.
+    assert torch.equal(
+        g.ndata[dgl.NTYPE],
+        torch.cat([torch.full((dataset.num_authors,), 0),
+                   torch.full((dataset.num_institutions,), 1),
+                   torch.full((dataset.num_papers,), 2)]))
+    assert torch.equal(
+        g.ndata[dgl.NID],
+        torch.cat([torch.arange(dataset.num_authors),
+                   torch.arange(dataset.num_institutions),
+                   torch.arange(dataset.num_papers)]))
+    g.edata['etype'] = g.edata[dgl.ETYPE].byte()
+    del g.edata[dgl.ETYPE]
+    del g.ndata[dgl.NTYPE]
+    del g.ndata[dgl.NID]
+
+    # Process feature
+    full_feat = np.memmap(
+        args.full_output_path, mode='w+', dtype='float16',
+        shape=(dataset.num_authors + dataset.num_institutions + dataset.num_papers, dataset.num_paper_features))
+    BLOCK_ROWS = 100000
+    for start in tqdm.trange(0, dataset.num_authors, BLOCK_ROWS):
+        end = min(dataset.num_authors, start + BLOCK_ROWS)
+        full_feat[author_offset + start:author_offset + end] = author_feat[start:end]
+    for start in tqdm.trange(0, dataset.num_institutions, BLOCK_ROWS):
+        end = min(dataset.num_institutions, start + BLOCK_ROWS)
+        full_feat[inst_offset + start:inst_offset + end] = inst_feat[start:end]
+    for start in tqdm.trange(0, dataset.num_papers, BLOCK_ROWS):
+        end = min(dataset.num_papers, start + BLOCK_ROWS)
+        full_feat[paper_offset + start:paper_offset + end] = paper_feat[start:end]
+
+# Convert the graph to the given format and save.  (The RGAT baseline needs CSC graph)
+g = g.formats(args.graph_format)
+dgl.save_graphs(args.graph_output_path, g)