Gb to dgl nb (#6764)

notebooks/graphbolt/walkthrough.ipynb

@@ -218,29 +218,6 @@
         "print(next(iter(datapipe)))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Gt059n1xrmj-"
-      },
-      "source": [
-        "After retrieving the required data, Graphbolt provides helper methods to convert it to the output format needed for subsequent GNN training.\n",
-        "\n",
-        "* Convert to **DGLMiniBatch** format for training with DGL."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "o8Yoi8BeqSdu"
-      },
-      "outputs": [],
-      "source": [
-        "datapipe = datapipe.to_dgl()\n",
-        "print(next(iter(datapipe)))"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/stochastic_training/link_prediction.ipynb

@@ -172,27 +172,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "In order to train with DGL, you need to convert `MiniBatch` to `DGLMiniBatch` like below:"
-      ],
-      "metadata": {
-        "id": "IpAgrEp_cdEP"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data = data.to_dgl()\n",
-        "print(f\"DGLMiniBatch: {data}\")"
-      ],
-      "metadata": {
-        "id": "KQgxFUyCcjVT"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "markdown",
       "source": [
@@ -263,38 +242,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Define utility function to vonvert the minibatch to a training pair and a label tensor.\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "J9K1GUs4ZDYw"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "def to_binary_link_dgl_computing_pack(data: gb.DGLMiniBatch):\n",
-        "    \"\"\"Convert the minibatch to a training pair and a label tensor.\"\"\"\n",
-        "    pos_src, pos_dst = data.positive_node_pairs\n",
-        "    neg_src, neg_dst = data.negative_node_pairs\n",
-        "    node_pairs = (\n",
-        "        torch.cat((pos_src, neg_src), dim=0),\n",
-        "        torch.cat((pos_dst, neg_dst), dim=0),\n",
-        "    )\n",
-        "    pos_label = torch.ones_like(pos_src)\n",
-        "    neg_label = torch.zeros_like(neg_src)\n",
-        "    labels = torch.cat([pos_label, neg_label], dim=0)\n",
-        "    return (node_pairs, labels.float())"
-      ],
-      "metadata": {
-        "id": "wvIJBPb7ZNUv"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "markdown",
       "source": [
@@ -313,11 +260,8 @@
         "    model.train()\n",
         "    total_loss = 0\n",
         "    for step, data in tqdm.tqdm(enumerate(train_dataloader)):\n",
-        "        # Convert to DGL format.\n",
-        "        data = data.to_dgl()\n",
-        "\n",
-        "        # Unpack DGLMiniBatch.\n",
-        "        compacted_pairs, labels = to_binary_link_dgl_computing_pack(data)\n",
+        "        # Get node pairs with labels for loss calculation.\n",
+        "        compacted_pairs, labels = data.node_pairs_with_labels\n",
         "        node_feature = data.node_features[\"feat\"]\n",
         "        # Convert sampled subgraphs to DGL blocks.\n",
         "        blocks = data.blocks\n",
@@ -369,11 +313,8 @@
         "logits = []\n",
         "labels = []\n",
         "for step, data in enumerate(eval_dataloader):\n",
-        "    # Convert to DGL format.\n",
-        "    data = data.to_dgl()\n",
-        "\n",
-        "    # Unpack MiniBatch.\n",
-        "    compacted_pairs, label = to_binary_link_dgl_computing_pack(data)\n",
+        "    # Get node pairs with labels for loss calculation.\n",
+        "    compacted_pairs, label = data.node_pairs_with_labels\n",
         "\n",
         "    # The features of sampled nodes.\n",
         "    x = data.node_features[\"feat\"]\n",

notebooks/stochastic_training/node_classification.ipynb

@@ -183,27 +183,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "In order to train with DGL, you need to convert `MiniBatch` to `DGLMiniBatch` like below:"
-      ],
-      "metadata": {
-        "id": "FwDJf1AJbNtt"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "data = data.to_dgl()\n",
-        "print(data)"
-      ],
-      "metadata": {
-        "id": "3Tzfp6A8bdWv"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "markdown",
       "source": [
@@ -336,7 +315,6 @@
         "\n",
         "    with tqdm.tqdm(train_dataloader) as tq:\n",
         "        for step, data in enumerate(tq):\n",
-        "            data = data.to_dgl()\n",
         "            x = data.node_features[\"feat\"]\n",
         "            labels = data.labels\n",
         "\n",
@@ -363,7 +341,6 @@
         "    labels = []\n",
         "    with tqdm.tqdm(valid_dataloader) as tq, torch.no_grad():\n",
         "        for data in tq:\n",
-        "            data = data.to_dgl()\n",
         "            x = data.node_features[\"feat\"]\n",
         "            labels.append(data.labels.cpu().numpy())\n",
         "            predictions.append(model(data.blocks, x).argmax(1).cpu().numpy())\n",