Tutorial: Build a Graph Convolutional Network on TVM (#3681)

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Co-Authored-By: 雾雨魔理沙 <lolisa@marisa.moe>

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tutorials/frontend/build_gcn.py

+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Building a Graph Convolutional Network
+=====================
+**Author**: `Yulun Yao <https://yulunyao.io/>`_
+
+This article is an introductory tutorial to build a Graph Convolutional Network (GCN) with Relay.
+
+In this tutorial, we will run our GCN on Cora dataset to demonstrate.
+
+Cora dataset is a common benchmark for Graph Neural Networks (GNN) and frameworks that support GNN training and inference.
+
+We directly load the dataset from DGL library to do the apples to apples comparison against DGL.
+
+Please refer to DGL tutorial on installation at
+https://docs.dgl.ai/install/index.html
+
+GPU support and more sparse operators will soon follow.
+"""
+
+######################################################################
+# Define Graph Convolution Layer
+# ----------------------------
+# To run GCN on TVM, we first need to implement Graph Convolution Layer.
+#
+# You may refer to https://github.com/dmlc/dgl/blob/master/python/dgl/nn/mxnet/conv.py for a GraphConv Layer implemented in DGL with MXNet Backend
+#
+# The layer is defined with below operations, note that we apply two transposes to keep adjacency matrix on right hand side of sparse_dense operator,
+# this method is temporary and will be updated in next few weeks when we have sparse matrix transpose and support for left sparse operator.
+#
+#  .. math::
+#
+#            \mbox{GraphConv}(A, H, W)   = A * H * W
+#                                        = ((H * W)^t * A^t)^t
+#                                        = ((W^t * H^t) * A^t)^t
+from tvm import relay
+
+def GraphConv(
+            layer_name,
+            input_dim,
+            output_dim,
+            adj,
+            input,
+            activation=None,
+            norm=None,
+            ):
+    r"""
+    Parameters
+    ----------
+    layer_name: str
+    Name of layer
+
+    input_dim: int
+    Input dimension per node feature
+
+    output_dim: int,
+    Output dimension per node feature
+
+    adj: namedtuple,
+    Graph representation (Adjacency Matrix) in Sparse Format (`data`, `indices`, `indptr`),
+    where `data` has shape [num_nonzeros], indices` has shape [num_nonzeros], `indptr` has shape [num_nodes + 1]
+
+    input: relay.Expr,
+    Input feature to current layer with shape [num_nodes, input_dim]
+
+    norm: relay.Expr,
+    Norm passed to this layer to normalize features before and after Convolution.
+
+    activation: <function relay.op.nn>,
+    Activation function applies to the output. e.g. relay.nn.{relu, sigmoid, log_softmax, softmax, leaky_relu}
+
+
+    Returns
+    ----------
+    output: tvm.relay.Expr
+    The Output Tensor for this layer [num_nodes, output_dim]
+    """
+    if norm is not None:
+        input = relay.multiply(input, norm)
+    weight = relay.var(layer_name + "_weight", shape=(input_dim, output_dim))
+    weight_transposed = relay.transpose(weight)
+    dense = relay.nn.dense(weight_transposed, input)
+    output = relay.nn.sparse_dense(dense, adj)
+    output_transposed = relay.transpose(output)
+    if norm is not None:
+        output_transposed = relay.multiply(output_transposed, norm)
+    if activation is not None:
+        output_transposed = activation(output_transposed)
+    return output_transposed
+
+######################################################################
+# Load the dataset
+# ------------------
+# You may substitute this part with your own dataset, here we load data from DGL to benchmark
+import tvm, dgl, scipy
+import numpy as np
+import networkx as nx
+from collections import namedtuple
+from dgl.data import load_data
+
+def load_dataset(dataset="cora"):
+    args = namedtuple("args", ["dataset"])
+    dataset = load_data(args(dataset))
+
+    params = {}
+    params['infeats'] = dataset.features.astype('float32') # Only support float32 as feature for now
+
+    # Remove self-loops to avoid duplicate passing of a node's feature to itself
+    g = dataset.graph
+    g.remove_edges_from(g.selfloop_edges())
+    g.add_edges_from(zip(g.nodes, g.nodes))
+
+    # Generate adjacency matrix
+    adjacency = nx.to_scipy_sparse_matrix(g)
+    params['data'] = adjacency.data.astype('float32')
+    params['indices'] = adjacency.indices.astype('int32')
+    params['indptr'] = adjacency.indptr.astype('int32')
+
+    # Normalization w.r.t. node degrees
+    degs = [g.in_degree[i] for i in range(g.number_of_nodes())]
+    params['norm'] = np.power(degs, -0.5).astype('float32')
+    params['norm'] = params['norm'].reshape((params['norm'].shape[0], 1))
+
+    return params
+
+######################################################################
+# Set up model Parameters
+# ------------------
+
+r"""
+Parameters
+----------
+num_hidden: int
+    number of hidden layers
+
+hidden_dim: int
+    input dimension of hidden layers
+
+num_classes: int
+    dimension of model output (Number of classes)
+
+target: str
+    currently only support llvm, GPU support will be added in next few weeks
+
+activation: <function relay.op.nn>,
+    Activation function applied to the output. e.g. relay.nn.{relu, sigmoid, log_softmax, softmax, leaky_relu}
+
+dataset: str
+    Name of dataset. You can pick from ['cora', 'citeseer', 'pubmed'] or you can use your own.
+"""
+
+num_hidden = 1
+hidden_dim = 16
+num_classes = 7
+target = 'llvm'
+activation = relay.nn.relu
+
+dataset = "cora"
+params = load_dataset(dataset)
+
+# Check shape of features
+assert len(params['infeats'].shape) == 2
+nnodes, input_dim = params['infeats'].shape
+
+# Check validity of adjacency matrix
+assert params['data'] is not None and params['indices'] is not None and params['indptr'] is not None
+assert nnodes == params['indptr'].shape[0] - 1
+
+######################################################################
+# Put layers together
+# ------------------
+
+layers = []
+
+# Define input features, norms, adjacency matrix
+infeats = relay.var("infeats", shape=(nnodes, input_dim))
+
+norm = relay.Constant(tvm.nd.array(params['norm']))
+
+data = relay.Constant(tvm.nd.array(params['data']))
+indices = relay.Constant(tvm.nd.array(params['indices']))
+indptr = relay.Constant(tvm.nd.array(params['indptr']))
+
+Adjacency = namedtuple('Adjacency', ['data', 'indices', 'indptr'])
+adj = Adjacency(data, indices, indptr)
+
+# Generate Input Layer
+layers.append(GraphConv(
+    layer_name= 'in',
+    input_dim= input_dim,
+    output_dim= hidden_dim,
+    adj = adj,
+    input= infeats,
+    activation= activation,
+    norm= norm,
+))
+
+# Generate Hidden Layers
+for i in range(num_hidden):
+    layers.append(GraphConv(
+        layer_name= str(i),
+        input_dim= hidden_dim,
+        output_dim= hidden_dim,
+        adj = adj,
+        input= layers[-1],
+        activation= activation,
+        norm= norm,
+    ))
+
+# Generate Output Layer
+layers.append(GraphConv(
+    layer_name= 'out',
+    input_dim= hidden_dim,
+    output_dim= num_classes,
+    adj = adj,
+    input= layers[-1],
+    activation= activation,
+    norm= norm,
+))
+output = layers[-1]
+
+# Analyze free variables and generate function
+func = relay.Function(relay.analysis.free_vars(output), output)
+
+######################################################################
+# Compile and run
+# ------------------
+# We achieved 6.5x speedup for this dataset against dgl given the same model parameters.
+# Output numerical difference < 10e-4 %.
+#
+# DGL version: https://github.com/dmlc/dgl/blob/master/examples/mxnet/gcn/gcn.py
+from tvm.contrib import graph_runtime
+import time
+
+# Set up weights. You can modify this part and use your own trained weights.
+params['in_weight'] = np.ones((input_dim, hidden_dim), dtype='float32')
+params['out_weight'] = np.ones((hidden_dim, num_classes), dtype='float32')
+for i in range(num_hidden):
+    params["%s_weight"%(str(i))] = np.ones((hidden_dim, hidden_dim), dtype='float32')
+
+# Generate graph and library
+with relay.build_config(opt_level=0): # Currently only support opt_level=0
+    graph, lib, params = relay.build(func, target, params=params)
+    lib.save("lib.o")
+
+# Generate module for llvm
+ctx = tvm.context(target, 0)
+m = graph_runtime.create(graph, lib, ctx)
+m.set_input(**params)
+
+print("finished compiling, testing inference time cost")
+totaltime = 0
+for i in range(30):
+    st = time.time()
+    # One forward pass on the entire network
+    m.run()
+    end = time.time()
+    # Retrieve output Tensor as numpy array
+    outval = m.get_output(0).asnumpy()
+
+    totaltime += (end-st)
+
+    if i == 0:
+        print("features of first five nodes \n %s" % outval[:5])
+    if i == 4:
+        print("5 Cycle Average Forward Pass Time ", totaltime/5)
+print("30 Cycle Average Forward Pass Time ", totaltime/30)