store complete model\nnot just the parameters but the entire instance of the model is saved to disk for lower code complexity when loading

1 year ago · 783ec89423
1 changed files with 61 additions and 49 deletions
--- a/AutomaticSentenceCompletion.ipynb
+++ b/AutomaticSentenceCompletion.ipynb
@ -44,7 +44,7 @@
  {
   "cell_type": "code",
   "execution_count": 2,
-   "id": "3209935b",
+   "id": "293027a6",
   "metadata": {},
   "outputs": [
    {
@ -101,7 +101,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
   "id": "00481ec9",
   "metadata": {},
   "outputs": [
@ -128,7 +128,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
   "id": "dcf5c217",
   "metadata": {},
   "outputs": [],
@ -149,10 +149,19 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 11,
   "id": "c3199444",
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/hein/.local/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
   "source": [
    "try:\n",
    "   import torch\n",
@ -176,7 +185,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 12,
   "id": "daca9db6",
   "metadata": {},
   "outputs": [],
@ -198,7 +207,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 13,
   "id": "8d2312db",
   "metadata": {},
   "outputs": [],
@ -216,7 +225,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 14,
   "id": "3f23404d",
   "metadata": {},
   "outputs": [],
@ -228,7 +237,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 15,
   "id": "8a128d3c",
   "metadata": {},
   "outputs": [],
@ -240,7 +249,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 16,
   "id": "97e89986",
   "metadata": {},
   "outputs": [],
@ -252,7 +261,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 17,
   "id": "0d6e89c4",
   "metadata": {},
   "outputs": [],
@ -272,7 +281,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 18,
   "id": "0bdbc40a",
   "metadata": {},
   "outputs": [],
@ -293,7 +302,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 19,
   "id": "a438ab1f",
   "metadata": {},
   "outputs": [],
@ -314,7 +323,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 20,
   "id": "0e5bc361",
   "metadata": {},
   "outputs": [],
@ -327,7 +336,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 21,
   "id": "dfd7400d",
   "metadata": {},
   "outputs": [],
@ -350,7 +359,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 22,
   "id": "c155ee31",
   "metadata": {},
   "outputs": [],
@ -389,7 +398,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 23,
   "id": "a33d722f",
   "metadata": {},
   "outputs": [],
@ -453,7 +462,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 24,
   "id": "c2f6d33b",
   "metadata": {},
   "outputs": [],
@ -490,7 +499,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 25,
   "id": "9e184841",
   "metadata": {},
   "outputs": [],
@ -514,7 +523,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 26,
   "id": "a4def1ac",
   "metadata": {},
   "outputs": [],
@ -536,7 +545,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 27,
   "id": "4ab5b8fd",
   "metadata": {},
   "outputs": [],
@ -568,7 +577,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 28,
   "id": "c53764da",
   "metadata": {},
   "outputs": [],
@ -592,7 +601,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 29,
   "id": "ddaa1d64",
   "metadata": {},
   "outputs": [],
@ -616,7 +625,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 30,
   "id": "50ab3fb6",
   "metadata": {},
   "outputs": [],
@ -665,7 +674,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 31,
   "id": "3d179bb0",
   "metadata": {},
   "outputs": [],
@ -1736,7 +1745,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "torch.save(best_model.state_dict(), \"autocomplete_model\")"
+    "torch.save(best_model, \"pubmed-sentencecomplete.pt\")"
   ]
  },
  {
@ -1757,14 +1766,13 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 32,
   "id": "cfb30fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_batch = [\n",
    "               \"The brain is\",\n",
-    "               \"The lung is\"\n",
    "]\n",
    "input_batch = sample_batch"
   ]
@ -1779,7 +1787,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 33,
   "id": "305853e8",
   "metadata": {},
   "outputs": [],
@ -1793,18 +1801,18 @@
   "id": "fe250072",
   "metadata": {},
   "source": [
-    "### define iterator for predict batch "
+    "### obtain iterator for predict batch "
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 34,
   "id": "afe585d6",
   "metadata": {},
   "outputs": [],
   "source": [
-    "def predict_abstract_iter():\n",
-    "    for batch in sample_batch:\n",
+    "def predict_abstract_iter(batch):\n",
+    "    for batch in batch:\n",
    "        yield tokenizer(batch)"
   ]
  },
@ -1818,13 +1826,13 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 35,
   "id": "8bfaa8bd",
   "metadata": {},
   "outputs": [],
   "source": [
-    "def toDataTensor():\n",
-    "    predict_generator = predict_abstract_iter()\n",
+    "def toDataTensor(batch):\n",
+    "    predict_generator = predict_abstract_iter(batch)\n",
    "    return [torch.tensor(vocab.lookup_indices(item)) for item in predict_generator]"
   ]
  },
@ -1838,7 +1846,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 36,
   "id": "6e2c35ba",
   "metadata": {},
   "outputs": [
@ -1848,7 +1856,7 @@
       "device(type='cuda')"
      ]
     },
-     "execution_count": 63,
+     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
@ -1868,23 +1876,26 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 40,
   "id": "223eed8a",
   "metadata": {},
   "outputs": [
    {
-     "data": {
-      "text/plain": [
-       "<All keys matched successfully>"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "RuntimeError",
+     "evalue": "Error(s) in loading state_dict for TransformerModel:\n\tsize mismatch for encoder.weight: copying a param with shape torch.Size([84399, 200]) from checkpoint, the shape in current model is torch.Size([6526, 200]).\n\tsize mismatch for decoder.weight: copying a param with shape torch.Size([84399, 200]) from checkpoint, the shape in current model is torch.Size([6526, 200]).\n\tsize mismatch for decoder.bias: copying a param with shape torch.Size([84399]) from checkpoint, the shape in current model is torch.Size([6526]).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [40], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m best_model \u001b[38;5;241m=\u001b[39m TransformerModel(ntokens, emsize, nhead, d_hid, nlayers, dropout)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mbest_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_state_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mautocomplete_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1667\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[0;34m(self, state_dict, strict)\u001b[0m\n\u001b[1;32m   1662\u001b[0m         error_msgs\u001b[38;5;241m.\u001b[39minsert(\n\u001b[1;32m   1663\u001b[0m             \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing key(s) in state_dict: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m   1664\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(k) \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m missing_keys)))\n\u001b[1;32m   1666\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(error_msgs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 1667\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mError(s) in loading state_dict for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m   1668\u001b[0m                        \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(error_msgs)))\n\u001b[1;32m   1669\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for TransformerModel:\n\tsize mismatch for encoder.weight: copying a param with shape torch.Size([84399, 200]) from checkpoint, the shape in current model is torch.Size([6526, 200]).\n\tsize mismatch for decoder.weight: copying a param with shape torch.Size([84399, 200]) from checkpoint, the shape in current model is torch.Size([6526, 200]).\n\tsize mismatch for decoder.bias: copying a param with shape torch.Size([84399]) from checkpoint, the shape in current model is torch.Size([6526])."
+     ]
    }
   ],
   "source": [
-    "best_model.load_state_dict(torch.load(\"autocomplete_model\"))"
+    "best_model = torch.load(\"pubmed-sentencecomplete.pt\")\n",
+    "best_model.eval()"
   ]
  },
  {
@ -1943,7 +1954,8 @@
    "        # 2*count is need because spaces count aswell\n",
    "        mask_size = bptt+(iteration)     \n",
    "        src_mask = generate_square_subsequent_mask(mask_size).to(device)\n",
-    "        data = toDataTensor()\n",
+    "        data = toDataTensor(input_batch)\n",
+    "        \n",
    "        for i, d in enumerate(data):\n",
    "            predictions = predict(d, src_mask, num_of_pred)\n",
    "            print(\"Current input:\", i)\n",