[v0.10.0 -> v0.13.0] Task11 (#230)

docs/how_to/relay/01-build_network.md

@@ -12,6 +12,11 @@ title: 构建图卷积网络
 
 本文介绍如何用 Relay 构建图卷积网络（GCN）。本教程演示在 Cora 数据集上运行 GCN。Cora 数据集是图神经网络（GNN）的 benchmark，同时是支持 GNN 训练和推理的框架。我们直接从 DGL 库加载数据集来与 DGL 进行同类比较。
 
+```bash
+pip install torch==2.0.0
+pip install dgl==v1.0.0
+```
+
 有关 DGL 安装，参阅 [DGL 文档](https://docs.dgl.ai/install/index.html)。
 
 有关 PyTorch 安装，参阅 [PyTorch 指南](https://pytorch.org/get-started/locally/)。
@@ -64,22 +69,12 @@ Using backend: pytorch
 from dgl.data import load_data
 from collections import namedtuple
 
-def load_dataset(dataset="cora"):
-    args = namedtuple("args", ["dataset"])
-    data = load_data(args(dataset))
-
-    # 删除自循环，避免重复将节点的特征传递给自身
-    g = data.graph
-    g.remove_edges_from(nx.selfloop_edges(g))
-    g.add_edges_from(zip(g.nodes, g.nodes))
-
-    return g, data
-
-def evaluate(data, logits):
-    test_mask = data.test_mask  # 未包含在训练阶段的测试集
+def evaluate(g, logits):
+    label = g.ndata["label"]
+    test_mask = g.ndata["test_mask"]
 
     pred = logits.argmax(axis=1)
-    acc = ((pred == data.labels) * test_mask).sum() / test_mask.sum()
+    acc = (torch.Tensor(pred[test_mask]) == label[test_mask]).float().mean()
 
     return acc
 ```
@@ -90,9 +85,6 @@ def evaluate(data, logits):
 """
 Parameters
 ----------
-dataset: str
-    Name of dataset. You can choose from ['cora', 'citeseer', 'pubmed'].
-
 num_layer: int
     number of hidden layers
 
@@ -106,13 +98,14 @@ num_classes: int
     dimension of model output (Number of classes)
 """
 
-dataset = "cora"
-g, data = load_dataset(dataset)
+dataset = dgl.data.CoraGraphDataset()
+dgl_g = dataset[0]
 
 num_layers = 1
 num_hidden = 16
-infeat_dim = data.features.shape[1]
-num_classes = data.num_labels
+features = dgl_g.ndata["feat"]
+infeat_dim = features.shape[1]
+num_classes = dataset.num_classes
 ```
 
 输出结果：
@@ -143,16 +136,14 @@ Done saving data into cached files.
 
 ``` python
 from tvm.contrib.download import download_testdata
-from dgl import DGLGraph
 
-features = torch.FloatTensor(data.features)
-dgl_g = DGLGraph(g)
+features = torch.FloatTensor(features)
 
 torch_model = GCN(dgl_g, infeat_dim, num_hidden, num_classes, num_layers, F.relu)
 
 # 下载预训练的权重
-model_url = "https://homes.cs.washington.edu/~cyulin/media/gnn_model/gcn_%s.torch" % (dataset)
-model_path = download_testdata(model_url, "gcn_%s.pickle" % (dataset), module="gcn_model")
+model_url = "https://homes.cs.washington.edu/~cyulin/media/gnn_model/gcn_cora.torch"
+model_path = download_testdata(model_url, "gcn_cora.pickle", module="gcn_model")
 
 # 将 weights 加载到模型中
 torch_model.load_state_dict(torch.load(model_path))
@@ -177,7 +168,7 @@ with torch.no_grad():
     logits_torch = torch_model(features)
 print("Print the first five outputs from DGL-PyTorch execution\n", logits_torch[:5])
 
-acc = evaluate(data, logits_torch.numpy())
+acc = evaluate(dgl_g, logits_torch.numpy())
 print("Test accuracy of DGL results: {:.2%}".format(acc))
 ```
 
@@ -270,26 +261,25 @@ def GraphConv(layer_name, input_dim, output_dim, adj, input, norm=None, bias=Tru
 import numpy as np
 import networkx as nx
 
-def prepare_params(g, data):
+def prepare_params(g):
     params = {}
-    params["infeats"] = data.features.numpy().astype(
-        "float32"
-    )  # 目前仅支持 float32 格式
+    params["infeats"] = g.ndata["feat"].numpy().astype("float32")
 
     # 生成邻接矩阵
-    adjacency = nx.to_scipy_sparse_matrix(g)
+    nx_graph = dgl.to_networkx(g)
+    adjacency = nx.to_scipy_sparse_array(nx_graph)
     params["g_data"] = adjacency.data.astype("float32")
     params["indices"] = adjacency.indices.astype("int32")
     params["indptr"] = adjacency.indptr.astype("int32")
 
     # 标准化 w.r.t.节点的度
-    degs = [g.in_degree[i] for i in range(g.number_of_nodes())]
+    degs = [g.in_degrees(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
 
-params = prepare_params(g, data)
+params = prepare_params(dgl_g)
 
 # 检查特征的 shape 和邻接矩阵的有效性
 assert len(params["infeats"].shape) == 2
@@ -310,7 +300,7 @@ assert params["infeats"].shape[0] == params["indptr"].shape[0] - 1
 
 ``` python
 # 在 Relay 中定义输入特征、范数、邻接矩阵
-infeats = relay.var("infeats", shape=data.features.shape)
+infeats = relay.var("infeats", shape=features.shape)
 norm = relay.Constant(tvm.nd.array(params["norm"]))
 g_data = relay.Constant(tvm.nd.array(params["g_data"]))
 indices = relay.Constant(tvm.nd.array(params["indices"]))
@@ -391,10 +381,7 @@ m.run()
 logits_tvm = m.get_output(0).numpy()
 print("Print the first five outputs from TVM execution\n", logits_tvm[:5])
 
-labels = data.labels
-test_mask = data.test_mask
-
-acc = evaluate(data, logits_tvm)
+acc = evaluate(dgl_g, logits_tvm)
 print("Test accuracy of TVM results: {:.2%}".format(acc))
 
 import tvm.testing

docs/how_to/relay/03-pipeline.md

@@ -21,12 +21,7 @@ from tvm.relay.op.contrib.cutlass import partition_for_cutlass
 from tvm import relay
 from tvm.relay import testing
 import tvm.testing
-from tvm.contrib.cutlass import (
-    has_cutlass,
-    num_cutlass_partitions,
-    finalize_modules,
-    finalize_modules_vm,
-)
+from tvm.contrib.cutlass import finalize_modules
 
 img_size = 8
 ```
@@ -44,7 +39,6 @@ def get_network():
         "dweight", relay.TensorType((batch_size, 16 * img_size * img_size), "float16")
     )
     weight = relay.var("weight")
-    second_weight = relay.var("second_weight")
     bn_gamma = relay.var("bn_gamma")
     bn_beta = relay.var("bn_beta")
     bn_mmean = relay.var("bn_mean")

docs/how_to/relay/04-relay_visualizer.md

@@ -16,6 +16,12 @@ Relay IR 模块可以包含很多操作。通常单个操作很容易理解，
 
 这里用渲染器来渲染文本形式的计算图，它是一个轻量级、类似 AST 可视化工具（灵感来自 [clang ast-dump](https://clang.llvm.org/docs/IntroductionToTheClangAST.html)）。以下将介绍如何通过接口类来实现自定义的解析器和渲染器。
 
+安装依赖可运行：
+
+```bash
+pip install graphviz
+```
+
 更多细节参考 `tvm.contrib.relay_viz`。
 
 ``` python