{"cells":[{"cell_type":"markdown","metadata":{"id":"VKuaeT-2F6H9"},"source":["## Reasoning Scaling Law Example Code for Training a Small Model"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"dEUhaFfkF6H_","executionInfo":{"status":"ok","timestamp":1751815271572,"user_tz":-480,"elapsed":36238,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}}},"outputs":[],"source":["### import relevant packages\n","\n","import networkx as nx\n","import numpy as np\n","import random\n","from collections import defaultdict\n","import os, json\n","import copy\n","import torch\n","import transformers\n","import matplotlib.pyplot as plt\n","import itertools\n","from transformers import Trainer, TrainingArguments\n","from torch.utils.data import IterableDataset, get_worker_info, Dataset\n","from typing import Dict, Optional, Sequence\n","from sklearn.utils import shuffle\n","from dataclasses import dataclass\n","import torch.nn.functional as F\n","from torch.nn import CrossEntropyLoss\n","from transformers import LlamaForCausalLM\n","from transformers.modeling_outputs import CausalLMOutputWithPast\n","from transformers.cache_utils import Cache\n","from typing import List, Optional, Tuple, Union"]},{"cell_type":"markdown","source":["Helper founctions:"],"metadata":{"id":"gA1eQp9T731U"}},{"cell_type":"code","source":["def set_seed(seed):\n"," torch.manual_seed(seed)\n"," torch.cuda.manual_seed(seed)\n"," torch.cuda.manual_seed_all(seed)\n"," np.random.seed(seed)\n"," random.seed(seed)\n"," torch.manual_seed(seed)\n","\n","def add_edge(G, h, t, r):\n"," num_edges = 0\n"," if G.has_edge(h, t):\n"," if r not in G[h][t]['id']:\n"," G[h][t]['id'].append(r)\n"," num_edges += 1\n"," else:\n"," print('edge already exists')\n"," else:\n"," G.add_edge(h, t, id=[r])\n"," num_edges += 1\n"," print('add edge: ', (h, r, t), 'num edges: ', num_edges)\n"," return num_edges\n","\n","\n","def generate_rules(relations, num_rules, L_min, L_max, weighted=False, temperature=0.25):\n"," # Generate K acyclic logic rules with varying lengths\n"," dependency_graph = defaultdict(set)\n"," rules = []\n"," weights = []\n"," if weighted:\n"," for l in range(L_min, L_max + 1):\n"," weights.append(np.exp(-temperature*l))\n"," probs = np.array([w / sum(weights) for w in weights])\n"," else:\n"," weights = [1] * (L_max - L_min + 1)\n","\n"," def has_cycle(start, visited, stack):\n"," \"\"\"Detects if adding a new dependency introduces a cycle.\"\"\"\n"," if start not in visited:\n"," visited.add(start)\n"," stack.add(start)\n"," print('visited: ', visited)\n"," print('stack: ', stack)\n"," for neighbor in dependency_graph[start]:\n"," if neighbor in stack:\n"," return True\n"," elif has_cycle(neighbor, visited, stack):\n"," return True\n"," if start in stack:\n"," stack.remove(start)\n"," return False\n","\n"," for _ in range(num_rules):\n"," while True:\n"," if weighted:\n"," length = random.choices(range(L_min, L_max + 1), weights=weights)[0]\n"," else:\n"," length = random.randint(L_min, L_max)\n"," rule_relations = random.choices(relations, k = length + 1) # the first element is the implied relation\n"," valid_rule = True\n"," for i in range(1, len(rule_relations)):\n"," dependency_graph[rule_relations[0]].add(rule_relations[i])\n","\n"," # Check for cycles\n"," if has_cycle(rule_relations[i], set(), set()):\n"," valid_rule = False\n"," for j in range(1, i + 1):\n"," dependency_graph[rule_relations[0]].remove(rule_relations[j])\n"," break\n","\n"," if valid_rule:\n"," rules.append(tuple(rule_relations))\n"," break\n","\n"," print('rules: ', rules)\n"," return rules\n","\n","def get_node_types(rules, max_num_relations_per_node=3):\n"," # map node types to out relations\n"," node_types = {}\n"," # map out relations to node types\n"," r2node_types = defaultdict(list)\n"," for rule in rules:\n"," for i in range(len(rule)):\n"," node_type = len(node_types)\n"," if i == 0:\n"," node_types[node_type] = [rule[i], rule[1]]\n"," r2node_types[rule[i]].append(node_type)\n"," r2node_types[rule[1]].append(node_type)\n"," elif i == len(rule) - 1:\n"," node_types[node_type] = ['-' + rule[i], '-' + rule[0]]\n"," r2node_types['-' + rule[i]].append(node_type)\n"," r2node_types['-' + rule[0]].append(node_type)\n"," else:\n"," node_types[node_type] = ['-' + rule[i], rule[i+1]]\n"," r2node_types['-' + rule[i]].append(node_type)\n"," r2node_types[rule[i+1]].append(node_type)\n","\n"," print(node_types)\n"," print(r2node_types)\n","\n"," for num_rs in range(2, max_num_relations_per_node):\n"," possible_new_node_types = []\n"," for r in r2node_types:\n"," alt_rs = []\n"," for node_type in r2node_types[r]:\n"," for _r in node_types[node_type]:\n"," if _r != r:\n"," alt_rs.append(_r)\n"," alt_rs = list(set(alt_rs))\n"," for node_type in r2node_types[r]:\n"," if len(node_types[node_type]) == num_rs:\n"," for _r in alt_rs:\n"," if _r not in node_types[node_type]:\n"," possible_new_node_types.append(tuple(sorted([_r] + list(node_types[node_type]))))\n"," print(possible_new_node_types)\n"," possible_new_node_types += list(set(possible_new_node_types))\n"," possible_new_node_types = list(set(possible_new_node_types))\n"," print(possible_new_node_types)\n","\n"," for rs in possible_new_node_types:\n"," new_node_type = len(node_types)\n"," node_types[new_node_type] = list(rs)\n"," for _r in rs:\n"," r2node_types[_r].append(new_node_type)\n","\n"," return node_types\n","\n","def get_adj_out_relations(rules):\n"," adj = defaultdict(list)\n"," for rule in rules:\n"," for i in range(len(rule)):\n"," if i == 0:\n"," adj[rule[i]].append(rule[1])\n"," adj[rule[1]].append(rule[i])\n"," elif i == len(rule) - 1:\n"," adj['-' + rule[i]].append('-' + rule[0])\n"," adj['-' + rule[0]].append('-' + rule[i])\n"," else:\n"," adj['-' + rule[i]].append(rule[i+1])\n"," adj[rule[i+1]].append('-' + rule[i])\n"," return adj"],"metadata":{"id":"MtrdtzEUHtgs","executionInfo":{"status":"ok","timestamp":1751815271602,"user_tz":-480,"elapsed":32,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["Synthetic graph generation:"],"metadata":{"id":"E-JuCbm_774w"}},{"cell_type":"code","source":["def latent_rule_graph(num_rules=50, L_min=2, L_max=4, n=10000, m=10, n_r=200,\n"," num_test=1000, num_train=150000, check_frequency=100,\n"," power_law=False, initial_graph=None,\n"," length_weighted=False, mcmc=0.2, temperature=0.25,\n"," deductible_ratio=0.5):\n","\n"," relations = ['P' + str(i) for i in range(n_r)]\n"," all_rules = generate_rules(relations, max(n_r//L_min, num_rules), L_min, L_max)\n"," r2rules = {}\n"," for rule in all_rules:\n"," if rule[0] not in r2rules:\n"," r2rules[rule[0]] = []\n"," r2rules[rule[0]].append(rule[1:])\n"," num_triples = 0\n"," repeated_entities = defaultdict(list) # map in relation to entities\n"," child_relations = []\n"," for rule in all_rules:\n"," child_relations += rule[1:]\n"," child_relations = list(set(child_relations))\n"," child_relations += ['-' + r for r in child_relations]\n"," deductible_rules = random.sample(all_rules, num_rules)\n"," if length_weighted:\n"," weights = [int(100*np.exp(-temperature*len(rule))) for rule in all_rules]\n"," else:\n"," weights = [1 for _ in all_rules]\n"," repeated_rules = []\n"," for rule, weight in zip(all_rules, weights):\n"," for _ in range(weight):\n"," repeated_rules.append(rule)\n"," random.shuffle(repeated_rules)\n"," adj = get_adj_out_relations(repeated_rules)\n"," all_deductibles = {}\n","\n"," if initial_graph is None:\n"," # Default initial graph\n"," G = nx.DiGraph()\n"," node_id = 0\n"," min_repeated_entities = 0\n"," while min_repeated_entities < m:\n"," for rule in all_rules:\n"," source = 'Q' + str(node_id)\n"," node_id += 1\n"," h = source\n"," for r in rule[1:]:\n"," t = 'Q' + str(node_id)\n"," node_id += 1\n"," num_triples += add_edge(G, h, t, r)\n"," repeated_entities[r].append(t)\n"," repeated_entities['-' + r].append(h)\n"," h = t\n"," num_triples += add_edge(G, source, t, rule[0])\n"," repeated_entities[rule[0]].append(t)\n"," repeated_entities['-' + rule[0]].append(source)\n","\n"," min_repeated_entities = min([len(set(repeated_entities[r])) for r in child_relations])\n"," else:\n"," if len(initial_graph) < m or len(initial_graph) > n:\n"," raise nx.NetworkXError(\n"," f\"Initial graph needs between m={m} and n={n} nodes\"\n"," )\n"," G = initial_graph.copy()\n"," node_id = len(G)\n","\n"," if not power_law:\n"," repeated_entities = {r: list(set(repeated_entities[r])) for r in repeated_entities}\n","\n"," # adding nodes\n"," while node_id < n:\n"," source = 'Q' + str(node_id)\n"," node_id += 1\n"," possible_relations = [_r for _r in adj if _r in child_relations]\n"," if len(possible_relations) == 0:\n"," print('no adj relations')\n"," break\n"," print('add child edge')\n"," chosen_edges = []\n"," stop = False\n"," for _ in range(m):\n"," it = 0\n"," while (r, t) in chosen_edges:\n"," r = random.choice(possible_relations)\n"," t = random.choice(repeated_entities[r])\n"," it += 1\n"," if it > 100:\n"," print('failed to find edge')\n"," stop = True\n"," break\n"," if stop or len(possible_relations) == 0:\n"," break\n","\n"," possible_relations = [_r for _r in adj[r] if _r in child_relations]\n"," chosen_edges.append((r, t))\n"," if r[0] == '-':\n"," num_triples += add_edge(G, t, source, r[1:])\n"," repeated_entities[r[1:]].append(source)\n"," else:\n"," num_triples += add_edge(G, source, t, r)\n"," repeated_entities['-' + r].append(source)\n"," repeated_entities[r].append(t)\n"," if len(possible_relations) == 0:\n"," print('no adj relations')\n"," break\n","\n"," if not power_law:\n"," repeated_entities = {r: list(set(repeated_entities[r])) for r in repeated_entities}\n","\n"," if node_id % check_frequency == 0 or node_id == n-1:\n"," # add deductibles\n"," all_nodes = list(G.nodes)\n"," random.shuffle(all_nodes)\n"," for h in all_nodes:\n"," for rule in deductible_rules:\n"," head_list = [h]\n"," r = rule[0]\n","\n"," for _r in rule[1:]:\n"," next_head_list = []\n"," for e_h in head_list:\n"," if e_h not in G.nodes:\n"," continue\n"," for e_t in G[e_h]:\n"," if _r in G[e_h][e_t]['id']:\n"," if random.random() < mcmc:\n"," next_head_list.append(e_t)\n"," head_list = next_head_list\n","\n"," for t in head_list:\n"," if (h, r, t) not in all_deductibles:\n"," all_deductibles[(h, r, t)] = [rule]\n"," elif rule not in all_deductibles[(h, r, t)]:\n"," all_deductibles[(h, r, t)].append(rule)\n"," if not G.has_edge(h, t) or r not in G[h][t]['id']:\n"," print('add deductible edge')\n"," add_edge(G, h, t, r)\n"," num_triples += 1\n"," repeated_entities[r].append(t)\n"," repeated_entities['-' + r].append(h)\n","\n"," atomic_triples = []\n"," deductible_triples = []\n"," for h, t in G.edges:\n"," for r in G[h][t]['id']:\n"," if (h, r, t) not in all_deductibles:\n"," atomic_triples.append((h, r, t))\n"," else:\n"," deductible_triples.append((h, r, t))\n"," random.shuffle(atomic_triples)\n"," random.shuffle(deductible_triples)\n"," assert len(atomic_triples) >= int(num_train * (1-deductible_ratio))\n"," assert len(deductible_triples) >= int(num_train * deductible_ratio) + 2 * num_test\n","\n"," remove_triples = []\n"," train_atomic_triples = atomic_triples[:int(num_train * (1-deductible_ratio))]\n"," remove_triples += atomic_triples[int(num_train * (1-deductible_ratio)):]\n"," train_deductible_triples = deductible_triples[:int(num_train * deductible_ratio)]\n"," remove_triples += deductible_triples[int(num_train * deductible_ratio):]\n","\n"," for h, r, t in remove_triples:\n"," _t = t\n"," rs = G[h][_t]['id']\n"," if r in rs:\n"," if len(rs) == 1:\n"," G.remove_edge(h, _t)\n"," else:\n"," G[h][_t]['id'].remove(r)\n","\n"," train_triples = train_deductible_triples + train_atomic_triples\n"," random.shuffle(train_triples)\n"," print(\"num train triples: \", len(train_triples))\n","\n"," r2rule = {}\n"," for rule in deductible_rules:\n"," if rule[0] in r2rule:\n"," r2rule[rule[0]].append(rule[1:])\n"," else:\n"," r2rule[rule[0]] = [rule[1:]]\n","\n"," def check_deductible(triple):\n"," h, r, t = triple\n"," alt_ts = []\n"," for rule in r2rule[r]:\n"," head_list = [h]\n"," for _r in rule:\n"," next_head_list = []\n"," for e_h in head_list:\n"," for e_t in G[e_h]:\n"," if _r in G[e_h][e_t]['id']:\n"," next_head_list.append(e_t)\n"," head_list = next_head_list\n"," alt_ts += head_list\n"," if t in alt_ts:\n"," return True\n"," return False\n","\n"," id_test_triples = []\n"," for i in range(int(num_train * deductible_ratio), len(deductible_triples)):\n"," if check_deductible(deductible_triples[i]):\n"," id_test_triples.append(deductible_triples[i])\n"," if len(id_test_triples) == num_test:\n"," break\n","\n"," id_test_rules = [all_deductibles[triple] for triple in id_test_triples]\n"," print(\"num id test triples: \", len(id_test_triples))\n","\n"," rule2triples = defaultdict(list)\n"," for triple in deductible_triples[i+1:]:\n"," for rule in all_deductibles[triple]:\n"," rule2triples[rule].append(triple)\n","\n"," # uniformly sample testing triples from each rule\n"," uniform_test_triples = []\n"," for rule in rule2triples:\n"," triples = []\n"," for triple in rule2triples[rule]:\n"," if check_deductible(triple):\n"," triples.append(triple)\n","\n"," if len(triples) > num_test//len(rule2triples):\n"," uniform_test_triples += random.sample(triples, num_test//len(rule2triples))\n"," else:\n"," uniform_test_triples += triples\n","\n"," random.shuffle(uniform_test_triples)\n"," uniform_test_rules = [all_deductibles[triple] for triple in uniform_test_triples]\n"," print(\"num uniform test triples: \", len(uniform_test_triples))\n","\n"," return G, deductible_rules, train_triples, id_test_triples, id_test_rules, uniform_test_triples, uniform_test_rules"],"metadata":{"id":"58tty8NWf4D0","executionInfo":{"status":"ok","timestamp":1751815271726,"user_tz":-480,"elapsed":114,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["Data class for synthetic graph:"],"metadata":{"id":"SxUZzyra8m7k"}},{"cell_type":"code","source":["class LatentRuleGraph:\n"," def __init__(self,\n"," n=1000, n_r=40, m=5, n_rules=30, n_triples=10000,\n"," num_test=1000, L_min=2, L_max=4, power_law=False,\n"," length_weighted=False, mcmc=1.0,\n"," temperature=0.25, deductible_ratio=0.5, seed=42):\n"," self.n = n\n"," self.n_r = n_r\n"," self.n_triples = n_triples\n"," self.n_rules = n_rules\n"," self.num_test = num_test\n"," self.L_min = L_min\n"," self.L_max = L_max\n"," self.power_law = power_law\n"," self.m = m\n"," self.length_weighted = length_weighted\n"," self.mcmc = mcmc\n"," self.temperature = temperature\n"," self.deductible_ratio = deductible_ratio\n"," self.seed = seed\n"," random.seed(seed)\n"," self.G = nx.DiGraph()\n"," self.load_data()\n"," self.all_es = list(self.G.nodes)\n"," self.all_rs = set()\n"," for h, t, r_dict in self.G.edges(data=True):\n"," for r in r_dict['id']:\n"," self.all_rs.add(r)\n"," self.triple_complet_file = None\n","\n"," def load_data(self):\n"," self.triples = []\n"," self.id_test_triples = []\n"," self.uniform_test_triples = []\n"," self.id_alt_ts = []\n"," self.uniform_alt_ts = []\n"," self.rules = []\n"," self.id_test_rules = []\n"," self.uniform_test_rules = []\n","\n","\n"," self.G, self.rules, self.triples, \\\n"," self.id_test_triples, self.id_test_rules, \\\n"," self.uniform_test_triples, self.uniform_test_rules = latent_rule_graph(\n"," num_rules=self.n_rules, L_min=self.L_min, L_max=self.L_max,\n"," n=self.n, n_r=self.n_r, m=self.m,\n"," num_test=self.num_test, num_train=self.n_triples,\n"," power_law=self.power_law,\n"," length_weighted=self.length_weighted, mcmc=self.mcmc,\n"," deductible_ratio=self.deductible_ratio, temperature=self.temperature)\n","\n"," r2rule = {}\n"," for rule in self.rules:\n"," if rule[0] in r2rule:\n"," r2rule[rule[0]].append(rule[1:])\n"," else:\n"," r2rule[rule[0]] = [rule[1:]]\n","\n"," def get_alt_ts(h, r, t):\n"," alt_ts = []\n"," for rule in r2rule[r]:\n"," head_list = [h]\n"," for _r in rule:\n"," next_head_list = []\n"," for e_h in head_list:\n"," for e_t in self.G[e_h]:\n"," if _r in self.G[e_h][e_t]['id']:\n"," next_head_list.append(e_t)\n"," head_list = next_head_list\n"," alt_ts += head_list\n"," return alt_ts\n","\n"," for h, r, t in self.id_test_triples:\n"," alt_ts = get_alt_ts(h, r, t)\n"," self.id_alt_ts.append(alt_ts)\n","\n"," for h, r, t in self.uniform_test_triples:\n"," alt_ts = get_alt_ts(h, r, t)\n"," self.uniform_alt_ts.append(alt_ts)\n","\n"," self.mem_triples = random.sample(self.triples, k=self.num_test)"],"metadata":{"id":"z-tRvGo6SJbY","executionInfo":{"status":"ok","timestamp":1751815271754,"user_tz":-480,"elapsed":25,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["Create a new synthetic graph:"],"metadata":{"id":"VwfKAW2q_bF9"}},{"cell_type":"code","source":["graph = LatentRuleGraph(\n"," n=2000,\n"," n_r=50,\n"," n_triples=10000,\n"," n_rules=20,\n"," L_min=2,\n"," L_max=4,\n"," power_law=True,\n"," deductible_ratio=0.5,\n"," length_weighted=False,\n"," m=6,\n"," num_test=1000,\n"," temperature=0.25,\n"," mcmc=1.0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u_l4knpm_YIH","executionInfo":{"status":"ok","timestamp":1751815284686,"user_tz":-480,"elapsed":12931,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}},"outputId":"0c9d6138-ea3b-4427-d33d-5f6c86bd5866"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","add edge: ('Q1681', 'P38', 'Q1821') num edges: 1\n","add deductible edge\n","add edge: ('Q1681', 'P38', 'Q1884') num edges: 1\n","add deductible edge\n","add edge: ('Q1681', 'P38', 'Q1885') num edges: 1\n","add deductible edge\n","add edge: ('Q1304', 'P43', 'Q1812') num edges: 1\n","add deductible edge\n","add edge: ('Q1304', 'P43', 'Q1819') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q663') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q521') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q412') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q1159') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q463') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q830') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q1307') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q1572') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q1724') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q1889') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q1279') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P22', 'Q1685') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P19', 'Q256') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P19', 'Q881') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P19', 'Q1139') num edges: 1\n","add deductible edge\n","add edge: ('Q1209', 'P19', 'Q747') num edges: 1\n","add deductible edge\n","add edge: ('Q971', 'P22', 'Q1501') num edges: 1\n","add deductible edge\n","add edge: ('Q971', 'P22', 'Q1889') num edges: 1\n","add deductible edge\n","add edge: ('Q971', 'P19', 'Q691') num edges: 1\n","add deductible edge\n","add edge: ('Q971', 'P19', 'Q1139') num edges: 1\n","add deductible edge\n","add edge: ('Q684', 'P22', 'Q1501') num edges: 1\n","add deductible edge\n","add edge: ('Q684', 'P22', 'Q1889') num edges: 1\n","add deductible edge\n","add edge: ('Q684', 'P19', 'Q1139') num edges: 1\n","add deductible edge\n","add edge: ('Q564', 'P4', 'Q1849') num edges: 1\n","add deductible edge\n","add edge: ('Q1137', 'P18', 'Q1813') num edges: 1\n","add deductible edge\n","add edge: ('Q728', 'P21', 'Q683') num edges: 1\n","add deductible edge\n","add edge: ('Q728', 'P21', 'Q988') num edges: 1\n","add deductible edge\n","add edge: ('Q927', 'P4', 'Q1803') num edges: 1\n","add deductible edge\n","add edge: ('Q927', 'P4', 'Q1849') num edges: 1\n","add deductible edge\n","add edge: ('Q1533', 'P29', 'Q449') num edges: 1\n","add deductible edge\n","add edge: ('Q1533', 'P29', 'Q700') num edges: 1\n","add deductible edge\n","add edge: ('Q1533', 'P29', 'Q918') num edges: 1\n","add deductible edge\n","add edge: ('Q1533', 'P29', 'Q1302') num edges: 1\n","add deductible edge\n","add edge: ('Q732', 'P18', 'Q1813') num edges: 1\n","add deductible edge\n","add edge: ('Q985', 'P18', 'Q1813') num edges: 1\n","add deductible edge\n","add edge: ('Q985', 'P18', 'Q50') num edges: 1\n","add deductible edge\n","add edge: ('Q984', 'P38', 'Q1874') num edges: 1\n","add deductible edge\n","add edge: ('Q1710', 'P4', 'Q1849') num edges: 1\n","add deductible edge\n","add edge: ('Q970', 'P43', 'Q1810') num edges: 1\n","add deductible edge\n","add edge: ('Q970', 'P43', 'Q1811') num edges: 1\n","add deductible edge\n","add edge: ('Q970', 'P43', 'Q1812') num edges: 1\n","add deductible edge\n","add edge: ('Q970', 'P43', 'Q1819') num edges: 1\n","add deductible edge\n","add edge: ('Q1031', 'P18', 'Q1813') num edges: 1\n","add deductible edge\n","add edge: ('Q633', 'P21', 'Q454') num edges: 1\n","add deductible edge\n","add 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edge\n","add edge: ('Q1773', 'P4', 'Q1932') num edges: 1\n","add deductible edge\n","add edge: ('Q945', 'P4', 'Q1932') num edges: 1\n","add deductible edge\n","add edge: ('Q713', 'P45', 'Q1928') num edges: 1\n","add deductible edge\n","add edge: ('Q1127', 'P44', 'Q394') num edges: 1\n","add deductible edge\n","add edge: ('Q1127', 'P44', 'Q920') num edges: 1\n","add deductible edge\n","add edge: ('Q1127', 'P44', 'Q1355') num edges: 1\n","add deductible edge\n","add edge: ('Q1127', 'P44', 'Q67') num edges: 1\n","add deductible edge\n","add edge: ('Q1127', 'P44', 'Q1957') num edges: 1\n","add deductible edge\n","add edge: ('Q1127', 'P22', 'Q1917') num edges: 1\n","add deductible edge\n","add edge: ('Q1127', 'P22', 'Q1918') num edges: 1\n","add deductible edge\n","add edge: ('Q1127', 'P22', 'Q1962') num edges: 1\n","add deductible edge\n","add edge: ('Q199', 'P38', 'Q1997') num edges: 1\n","add deductible edge\n","add edge: ('Q199', 'P38', 'Q1912') num edges: 1\n","add deductible edge\n","add edge: ('Q199', 'P38', 'Q1975') num edges: 1\n","add deductible edge\n","add edge: ('Q199', 'P38', 'Q1976') num edges: 1\n","add deductible edge\n","add edge: ('Q1711', 'P4', 'Q1932') num edges: 1\n","add deductible edge\n","add edge: ('Q187', 'P43', 'Q425') num edges: 1\n","add deductible edge\n","add edge: ('Q169', 'P44', 'Q1957') num edges: 1\n","add deductible edge\n","add edge: ('Q169', 'P22', 'Q876') num edges: 1\n","add deductible edge\n","add edge: ('Q367', 'P21', 'Q993') num edges: 1\n","add deductible edge\n","add edge: ('Q367', 'P21', 'Q212') num edges: 1\n","add deductible edge\n","add edge: ('Q367', 'P21', 'Q740') num edges: 1\n","add deductible edge\n","add edge: ('Q747', 'P18', 'Q1927') num edges: 1\n","add deductible edge\n","add edge: ('Q60', 'P44', 'Q1957') num edges: 1\n","add child edge\n","add edge: ('Q1999', 'P24', 'Q89') num edges: 1\n","add edge: ('Q1999', 'P37', 'Q923') num edges: 1\n","add edge: ('Q1718', 'P26', 'Q1999') num edges: 1\n","add edge: ('Q1999', 'P38', 'Q1513') num edges: 1\n","add edge: ('Q1999', 'P17', 'Q698') num edges: 1\n","add edge: ('Q1999', 'P38', 'Q144') num edges: 1\n","add deductible edge\n","add edge: ('Q20', 'P15', 'Q386') num edges: 1\n","add deductible edge\n","add edge: ('Q20', 'P15', 'Q1091') num edges: 1\n","add deductible edge\n","add edge: ('Q20', 'P15', 'Q848') num edges: 1\n","add deductible edge\n","add edge: ('Q1060', 'P21', 'Q993') num edges: 1\n","add deductible edge\n","add edge: ('Q1060', 'P21', 'Q212') num edges: 1\n","add deductible edge\n","add edge: ('Q1060', 'P21', 'Q740') num edges: 1\n","add deductible edge\n","add edge: ('Q760', 'P21', 'Q563') num edges: 1\n","add deductible edge\n","add edge: ('Q760', 'P21', 'Q993') num edges: 1\n","add deductible edge\n","add edge: ('Q760', 'P21', 'Q740') num edges: 1\n","add deductible edge\n","add edge: ('Q1184', 'P15', 'Q386') num edges: 1\n","add deductible edge\n","add edge: ('Q1184', 'P15', 'Q1091') num edges: 1\n","add deductible edge\n","add edge: ('Q1184', 'P15', 'Q848') num edges: 1\n","add deductible edge\n","add edge: ('Q1235', 'P21', 'Q563') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q354') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q875') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q876') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q663') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q521') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q412') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q1159') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q463') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q830') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q1307') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q1572') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q1724') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q1889') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q1279') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q1685') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P22', 'Q1486') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P19', 'Q256') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P19', 'Q881') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P19', 'Q1139') num edges: 1\n","add deductible edge\n","add edge: ('Q1967', 'P19', 'Q747') num edges: 1\n","add deductible edge\n","add edge: ('Q1948', 'P22', 'Q354') num edges: 1\n","add deductible edge\n","add edge: ('Q1283', 'P20', 'Q272') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q875') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q876') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q663') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q521') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q412') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q1159') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q463') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q830') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q1307') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q1724') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q1889') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q1279') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q1685') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P22', 'Q1486') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P19', 'Q256') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P19', 'Q881') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P19', 'Q1139') num edges: 1\n","add deductible edge\n","add edge: ('Q1966', 'P19', 'Q747') num edges: 1\n","add deductible edge\n","add edge: ('Q703', 'P21', 'Q661') num edges: 1\n","add deductible edge\n","add edge: ('Q703', 'P21', 'Q1119') num edges: 1\n","add deductible edge\n","add edge: ('Q703', 'P21', 'Q1384') num edges: 1\n","add deductible edge\n","add edge: ('Q1107', 'P21', 'Q1999') num edges: 1\n","add deductible edge\n","add edge: ('Q813', 'P20', 'Q272') num edges: 1\n","add deductible edge\n","add edge: ('Q1152', 'P44', 'Q176') num edges: 1\n","add deductible edge\n","add edge: ('Q1152', 'P44', 'Q1728') num edges: 1\n","add deductible edge\n","add edge: ('Q1146', 'P21', 'Q1999') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q245') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q848') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1093') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1094') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1158') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1159') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1260') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1275') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1279') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1335') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1487') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1687') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1688') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1888') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q776') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q778') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q136') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1091') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1110') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1307') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1334') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1353') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1354') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1435') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1501') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1679') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1744') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1917') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1918') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q572') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1962') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q521') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q665') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q664') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1879') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q1880') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P22', 'Q830') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q365') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q38') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q691') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q881') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q747') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q839') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q256') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q1633') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q1139') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q1476') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q147') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q836') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q837') num edges: 1\n","add deductible edge\n","add edge: ('Q1911', 'P19', 'Q1867') num edges: 1\n","add deductible edge\n","add edge: ('Q151', 'P15', 'Q1091') num edges: 1\n","add deductible edge\n","add edge: ('Q151', 'P15', 'Q848') num edges: 1\n","add deductible edge\n","add edge: ('Q258', 'P21', 'Q993') num edges: 1\n","add deductible edge\n","add edge: ('Q258', 'P21', 'Q212') num edges: 1\n","add deductible edge\n","add edge: ('Q258', 'P21', 'Q740') num edges: 1\n","add deductible edge\n","add edge: ('Q258', 'P21', 'Q1316') num edges: 1\n","add deductible edge\n","add edge: ('Q258', 'P21', 'Q1432') num edges: 1\n","add deductible edge\n","add edge: ('Q758', 'P21', 'Q1999') num edges: 1\n","add deductible edge\n","add edge: ('Q1179', 'P44', 'Q394') num edges: 1\n","add deductible edge\n","add edge: ('Q1179', 'P44', 'Q920') num edges: 1\n","add deductible edge\n","add edge: ('Q1179', 'P44', 'Q1355') num edges: 1\n","add deductible edge\n","add edge: ('Q1419', 'P21', 'Q1999') num edges: 1\n","add deductible edge\n","add edge: ('Q751', 'P15', 'Q386') num edges: 1\n","add deductible edge\n","add edge: ('Q751', 'P15', 'Q1091') num edges: 1\n","add deductible edge\n","add edge: ('Q751', 'P15', 'Q848') num edges: 1\n","add deductible edge\n","add edge: ('Q1272', 'P21', 'Q1999') num edges: 1\n","add deductible edge\n","add edge: ('Q1973', 'P45', 'Q649') num edges: 1\n","add deductible edge\n","add edge: ('Q1973', 'P45', 'Q1211') num edges: 1\n","add deductible edge\n","add edge: ('Q1973', 'P45', 'Q259') num edges: 1\n","add deductible edge\n","add edge: ('Q1973', 'P45', 'Q1120') num edges: 1\n","add deductible edge\n","add edge: ('Q1973', 'P45', 'Q416') num edges: 1\n","add deductible edge\n","add edge: ('Q1973', 'P45', 'Q729') num edges: 1\n","add deductible edge\n","add edge: ('Q1973', 'P45', 'Q1166') num edges: 1\n","add deductible edge\n","add edge: ('Q1973', 'P45', 'Q1622') num edges: 1\n","add deductible edge\n","add edge: ('Q663', 'P15', 'Q386') num edges: 1\n","add deductible edge\n","add edge: ('Q663', 'P15', 'Q1091') num edges: 1\n","add deductible edge\n","add edge: ('Q663', 'P15', 'Q848') num edges: 1\n","add deductible edge\n","add edge: ('Q1835', 'P21', 'Q993') num edges: 1\n","add deductible edge\n","add edge: ('Q1835', 'P21', 'Q212') num edges: 1\n","add deductible edge\n","add edge: ('Q1835', 'P21', 'Q740') num edges: 1\n","add deductible edge\n","add edge: ('Q1145', 'P21', 'Q1999') num edges: 1\n","add deductible edge\n","add edge: ('Q182', 'P21', 'Q1999') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q304') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q205') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1056') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1680') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1808') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q631') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q806') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q853') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q854') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q952') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q413') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q71') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q784') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q785') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q795') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q796') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q852') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1191') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1192') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1483') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1484') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q816') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q181') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1110') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1111') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1584') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1585') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q1609') num edges: 1\n","add deductible edge\n","add edge: ('Q1999', 'P38', 'Q616') num edges: 1\n","add deductible edge\n","add edge: ('Q1959', 'P47', 'Q368') num edges: 1\n","add deductible edge\n","add edge: ('Q1959', 'P47', 'Q377') num edges: 1\n","add deductible edge\n","add edge: ('Q1959', 'P47', 'Q1770') num edges: 1\n","add deductible edge\n","add edge: ('Q708', 'P45', 'Q150') num edges: 1\n","add deductible edge\n","add edge: ('Q472', 'P44', 'Q176') num edges: 1\n","add deductible edge\n","add edge: ('Q472', 'P44', 'Q1728') num edges: 1\n","add deductible edge\n","add edge: ('Q1908', 'P22', 'Q354') num edges: 1\n","add deductible edge\n","add edge: ('Q716', 'P44', 'Q176') num edges: 1\n","add deductible edge\n","add edge: ('Q716', 'P44', 'Q1728') num edges: 1\n","add deductible edge\n","add edge: ('Q655', 'P21', 'Q1999') num edges: 1\n","add deductible edge\n","add edge: ('Q715', 'P44', 'Q176') num edges: 1\n","add deductible edge\n","add edge: ('Q715', 'P44', 'Q1728') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q342') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q729') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q705') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q1212') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q1213') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q1225') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q213') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q659') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q660') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q985') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q1220') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q89') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q259') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q1120') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q416') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q1166') num edges: 1\n","add deductible edge\n","add edge: ('Q1972', 'P45', 'Q1622') num edges: 1\n","add deductible edge\n","add edge: ('Q1200', 'P6', 'Q923') num edges: 1\n","num train triples: 10000\n","num id test triples: 1000\n","num uniform test triples: 660\n"]}]},{"cell_type":"markdown","source":["Training data class for synthetic graph:"],"metadata":{"id":"0Usl6GWN8s0g"}},{"cell_type":"code","source":["class TrainDataset(IterableDataset):\n"," \"\"\"\n"," Iterable dataset that returns constant length chunks of tokens from stream of text files.\n"," Args:\n"," tokenizer (Tokenizer): The processor used for proccessing the data.\n"," dataset (dataset.Dataset): Dataset with text files.\n"," infinite (bool): If True the iterator is reset after dataset reaches end else stops.\n"," seq_length (int): Length of token sequences to return.\n"," num_of_sequences (int): Number of token sequences to keep in buffer.\n"," chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.\n"," tokenized (bool): If true we use a pretokenized dataset.\n"," \"\"\"\n","\n"," def __init__(\n"," self,\n"," graph, # generated graph\n"," tokenizer,\n"," seq_length=256,\n"," num_of_sequences=1024,\n"," chars_per_token=3.6,\n"," seed=42,\n"," ):\n"," super(TrainDataset, self).__init__()\n","\n"," self.tokenizer = tokenizer\n"," self.seq_length = seq_length\n"," self.epoch = 0\n"," self.current_size = 0\n"," self.num_buffer_sequences = num_of_sequences\n"," self.max_buffer_size = seq_length * chars_per_token * num_of_sequences\n"," self.seed = seed\n"," self.data = graph\n","\n"," print(\"max buffer size: \", self.max_buffer_size)\n","\n"," def set_epoch(self, worker_id):\n"," set_seed(self.seed + self.epoch + worker_id) # int(time.time())\n","\n"," def triple2str(self, triple):\n"," if type(triple[0]) == int or type(triple[1]) == int or type(triple[2]) == int:\n"," return f'Q{triple[0]} P{triple[1]} Q{triple[2]}'\n"," else:\n"," return ' '.join(list(triple))\n","\n"," def iter_fun(self, worker_id=0):\n"," num_sents = len(self.data.triples)\n"," while True:\n"," i = random.randint(0, num_sents-1)\n"," triple = self.data.triples[i]\n"," text = self.triple2str(triple) + '\\n'\n"," if text is None:\n"," print(\"cannot translate \", triple, \" into text.\")\n"," continue\n"," yield text\n","\n"," def __len__(self):\n"," return len(self.data.triples)\n","\n"," def __iter__(self):\n"," more_examples = True\n"," try:\n"," worker_info = get_worker_info()\n"," print(worker_info)\n"," worker_id = worker_info.id\n"," except:\n"," worker_id = 0\n"," self.set_epoch(worker_id)\n"," iterator = self.iter_fun(worker_id=worker_id)\n"," print(\"worker id: \", )\n","\n"," while more_examples:\n"," buffer, buffer_len = [], 0\n"," while True:\n"," if buffer_len >= self.max_buffer_size:\n"," print(\"data buffer full\")\n"," break\n"," try:\n"," buffer.append(next(iterator))\n"," buffer_len += len(buffer[-1])\n"," except StopIteration:\n"," self.epoch += 1\n"," self.set_epoch(worker_id)\n"," iterator = self.iter_fun()\n"," print(f\"Dataset epoch: {self.epoch}\")\n"," # print(buffer[:3])\n","\n"," input_lens = []\n"," random.shuffle(buffer)\n"," tokenized_inputs = self.tokenizer(buffer,\n"," padding=False,\n"," max_length=self.seq_length,\n"," truncation=True)[\"input_ids\"]\n"," for tokenized_input in tokenized_inputs:\n"," input_ids = tokenized_input + [self.tokenizer.eos_token_id]\n"," input_lens.append(len(input_ids))\n"," self.current_size += 1\n"," yield dict(input_ids=torch.tensor(input_ids), labels=torch.tensor(input_ids))\n"," print(\"average example length: \", np.mean(input_lens))"],"metadata":{"id":"iAKmfbSrR5in","executionInfo":{"status":"ok","timestamp":1751815284702,"user_tz":-480,"elapsed":6,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["train_dataset = TrainDataset(\n"," graph,\n"," tokenizer=None,\n"," seq_length=128,\n"," num_of_sequences=1024,\n"," chars_per_token=3.6,\n"," )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B4sdu4gs_Zyq","executionInfo":{"status":"ok","timestamp":1751815284717,"user_tz":-480,"elapsed":8,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}},"outputId":"e36eb113-b35c-49f7-ba4b-e1476a37450c"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["max buffer size: 471859.2\n"]}]},{"cell_type":"markdown","source":["Tokenizer class:"],"metadata":{"id":"aY8D6o8N8vlQ"}},{"cell_type":"code","source":["class BaseTokenizer:\n"," def __init__(self, n=1, vocab=None, padding_side='right', add_special_tokens=False):\n"," self.n = n\n"," if vocab is None:\n"," self.vocab = self.build_vocab()\n"," else:\n"," self.vocab = vocab\n"," self.rev_vocab = {v: k for k, v in self.vocab.items()}\n"," self.padding_side = padding_side\n"," self.add_special_tokens = add_special_tokens\n"," self.bos_token = ''\n"," self.bos_token_id = self.vocab['']\n"," self.eos_token = ''\n"," self.eos_token_id = self.vocab['']\n"," self.pad_token = ''\n"," self.pad_token_id = self.vocab['']\n"," self.unk_token = ''\n"," self.unk_token_id = self.vocab['']\n"," self.all_special_ids = [self.bos_token_id, self.eos_token_id,\n"," self.pad_token_id, self.unk_token_id]\n"," self.all_special_tokens = self.all_special_tokens_extended = [\n"," self.bos_token, self.eos_token,\n"," self.pad_token, self.unk_token]\n","\n"," def build_vocab(self):\n"," pass\n","\n"," def tokenize(self, text: str, max_length: int):\n"," pass\n","\n"," def encode(self, text, padding=False, max_length=1024, return_tensors=None, truncation=True):\n"," if type(text) == str:\n"," ids = [self.tokenize(text, max_length)]\n"," else:\n"," ids = []\n"," lens = []\n"," for t in text:\n"," _ids = self.tokenize(t, max_length)\n"," ids.append(_ids)\n"," lens.append(len(_ids))\n","\n"," if padding:\n"," max_length = max(lens)\n"," for _ids in ids:\n"," if len(_ids) < max_length:\n"," if self.padding_side == 'left':\n"," _ids = [self.pad_token_id] * (max_length - len(_ids)) + _ids\n"," elif self.padding_side == 'right':\n"," _ids += [self.pad_token_id] * (max_length - len(_ids))\n"," else:\n"," raise NotImplementedError\n","\n"," if return_tensors == 'pt':\n"," ids = torch.tensor(ids)\n","\n"," return ids\n","\n"," def __call__(self, text, padding=False, max_length=1024, return_tensors=None, truncation=True, device='cpu'):\n"," if type(text) == str:\n"," ids = [self.tokenize(text, max_length)]\n"," attns = [[1] * len(ids[0])]\n"," else:\n"," ids = []\n"," attns = []\n"," lens = []\n"," for t in text:\n"," _ids = self.tokenize(t, max_length)\n"," ids.append(_ids)\n"," lens.append(len(_ids))\n"," attns.append([1] * len(_ids))\n","\n"," if padding:\n"," max_length = max(lens)\n"," padded_ids = []\n"," padded_attns = []\n"," for _ids, attn in zip(ids, attns):\n"," num_pad = max_length - len(_ids)\n"," if self.padding_side == 'left':\n"," padded_ids.append([self.pad_token_id] * num_pad + _ids)\n"," padded_attns.append([0] * num_pad + attn)\n"," elif self.padding_side == 'right':\n"," padded_ids.append(_ids + [self.pad_token_id] * num_pad)\n"," padded_attns.append(attn + [0] * num_pad)\n"," else:\n"," raise NotImplementedError\n"," ids = padded_ids\n"," attns = padded_attns\n","\n"," if return_tensors == 'pt':\n"," ids = torch.tensor(ids).to(device)\n"," attns = torch.tensor(attns).to(device)\n","\n"," return {\"input_ids\": ids, 'attention_mask': attns}\n","\n"," def __len__(self):\n"," return len(self.vocab)\n","\n"," def decode(self, token_ids, skip_special_tokens=False):\n"," if type(token_ids) == int:\n"," return self.rev_vocab[token_ids]\n"," else:\n"," out = ''\n"," for i in token_ids:\n"," if i == self.eos_token_id:\n"," if not skip_special_tokens:\n"," out += self.eos_token\n"," break\n"," if skip_special_tokens and i in self.all_special_ids:\n"," continue\n"," out += self.rev_vocab[i]\n"," return out\n","\n"," def batch_decode(self, sequences, skip_special_tokens=False):\n"," out = []\n"," for token_ids in sequences:\n"," out.append(self.decode(token_ids, skip_special_tokens))\n"," return out\n","\n"," def save_pretrained(self, output_dir):\n"," with open(f'{output_dir}/tokenizer.json', 'w') as wf:\n"," json.dump(self.vocab, wf, indent = 4)\n","\n"," @classmethod\n"," def from_pretrained(cls, pretrained_model_name_or_path, padding_side='right', trust_remote_code=False, revision=None):\n"," vocab_path = f\"{pretrained_model_name_or_path}/tokenizer.json\"\n"," if os.path.exists(vocab_path):\n"," vocab = json.load(open(vocab_path))\n"," n = 1\n"," for token in vocab:\n"," if token not in ['', '', '', '']:\n"," if '_' in token:\n"," n = max(n, int(token.split('_')[1]) + 1)\n"," else:\n"," n = max(n, len(token))\n","\n"," return cls(n, vocab, padding_side=padding_side)\n"," else:\n"," return cls(padding_side=padding_side)\n","\n","class CharTokenizer(BaseTokenizer):\n"," def __init__(self, n=1, vocab=None, padding_side='right', add_special_tokens=False):\n"," super().__init__(n, vocab, padding_side, add_special_tokens)\n","\n"," def build_vocab(self):\n"," vocab = {'Q':0, 'P':1}\n"," for i in range(10):\n"," vocab[str(i)] = i+2\n"," vocab_size = 12\n"," vocab['\\n'] = vocab_size\n"," vocab_size += 1\n"," vocab[' '] = vocab_size\n"," vocab_size += 1\n"," vocab['-'] = vocab_size\n"," vocab_size += 1\n"," vocab['?'] = vocab_size\n"," vocab_size += 1\n"," vocab[''] = vocab_size\n"," vocab_size += 1\n"," vocab[''] = vocab_size\n"," vocab_size += 1\n"," vocab[''] = vocab_size\n"," vocab_size += 1\n"," vocab[''] = vocab_size\n","\n"," return vocab\n","\n"," def tokenize(self, text: str, max_length: int):\n"," ids = []\n"," for l in text.split('\\n'):\n"," if len(l) == 0:\n"," continue\n"," for w in l.split():\n"," for c in w.strip():\n"," if c not in self.vocab:\n"," ids.append(self.unk_token_id)\n"," else:\n"," ids.append(self.vocab[c])\n"," ids.append(self.vocab[' '])\n"," ids.append(self.vocab['\\n'])\n","\n"," if self.add_special_tokens:\n"," ids.append(self.vocab[''])\n"," else:\n"," ids = ids[:-2]\n"," # print(ids)\n"," if max_length < len(ids):\n"," return ids[:max_length]\n"," else:\n"," return ids"],"metadata":{"id":"JWZC4rdF8oGZ","executionInfo":{"status":"ok","timestamp":1751815284764,"user_tz":-480,"elapsed":46,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}}},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":["Helper functions for training and evaluation:"],"metadata":{"id":"Sdn-qc9783PF"}},{"cell_type":"code","source":["IGNORE_INDEX = -100\n","DEFAULT_PAD_TOKEN = \"[PAD]\"\n","DEFAULT_EOS_TOKEN = \"\"\n","DEFAULT_BOS_TOKEN = \"\"\n","DEFAULT_UNK_TOKEN = \"\"\n","\n","def model_path_map(model_name):\n"," return '../llms/' + model_name\n","\n","def count_params(model):\n"," params: int = sum(p.numel() for p in model.parameters() if p.requires_grad)\n"," return params\n","\n","def compute_llama_param(l, h, v):\n"," d = 64 * h\n"," embd = d * v\n"," atten = 4*d*d\n"," mlp = 2*d*d*3\n"," ln = d\n"," return l * (atten + mlp + 2*ln) + ln + 2*embd\n","\n","@dataclass\n","class DataCollatorForSupervisedDataset(object):\n"," \"\"\"Collate examples for supervised fine-tuning.\"\"\"\n","\n"," tokenizer: transformers.PreTrainedTokenizer\n","\n"," def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:\n"," input_ids, labels = tuple([instance[key] for instance in instances]\n"," for key in (\"input_ids\", \"labels\"))\n"," input_ids = torch.nn.utils.rnn.pad_sequence(\n"," input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id\n"," )\n"," labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True,\n"," padding_value=IGNORE_INDEX)\n","\n"," attn_mask = input_ids.ne(self.tokenizer.pad_token_id)\n","\n"," # print(\"input_ids: \", input_ids)\n"," # print(\"labels: \", labels)\n"," # print(\"atten mask: \", attn_mask)\n","\n"," return dict(\n"," input_ids=input_ids,\n"," labels=labels,\n"," attention_mask=attn_mask,\n"," )\n","\n","\n","def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, max_length: int) -> Dict:\n"," \"\"\"Tokenize a list of strings.\"\"\"\n"," tokenized_list = [\n"," tokenizer(\n"," text,\n"," return_tensors=\"pt\",\n"," padding=\"longest\",\n"," max_length=max_length,\n"," truncation=True,\n"," # pad_to_multiple_of=8,\n"," )[\"input_ids\"]\n"," for text in strings\n"," ]\n"," input_ids = labels = [tokenized[0] for tokenized in tokenized_list]\n"," input_ids_lens = labels_lens = [\n"," tokenized.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list\n"," ]\n"," return dict(\n"," input_ids=input_ids,\n"," labels=labels,\n"," input_ids_lens=input_ids_lens,\n"," labels_lens=labels_lens,\n"," )\n","\n","\n","def prepare_data(\n"," sources: Sequence[str],\n"," targets: Sequence[str],\n"," tokenizer: transformers.PreTrainedTokenizer,\n"," max_length: int\n",") -> Dict:\n"," \"\"\"Preprocess the data by tokenizing.\"\"\"\n"," examples = [s + t for s, t in zip(sources, targets)]\n"," examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer, max_length)\n"," for strings in (examples, sources)]\n"," eos = torch.tensor([tokenizer.eos_token_id])\n"," input_ids = [torch.cat((ids, eos)) for ids in examples_tokenized[\"input_ids\"]]\n"," labels = copy.deepcopy(input_ids)\n"," for label, source_len in zip(labels, sources_tokenized[\"input_ids_lens\"]):\n"," label[:source_len] = IGNORE_INDEX\n"," return dict(input_ids=input_ids, labels=labels)"],"metadata":{"id":"5j7oSdDU-mdE","executionInfo":{"status":"ok","timestamp":1751815284797,"user_tz":-480,"elapsed":22,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}}},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":["Training function (set bf16 to True if you have GPUs):"],"metadata":{"id":"_4SzSxLe8_Dh"}},{"cell_type":"code","source":["def train(train_dataset, model_name_or_path='llama-2-2', random_initialize=True,\n"," output_dir='.', bf16=False):\n","\n"," set_seed(42) # make sure use the same model initialization\n"," l, h, v = None, None, None\n","\n"," if random_initialize:\n"," print(\"Random initializing...\")\n"," model_name, l, h = model_name_or_path.split('-')\n"," l, h = int(l), int(h)\n"," d = 64 * h\n"," if model_name == 'llama':\n"," config = transformers.LlamaConfig(hidden_size=d,\n"," intermediate_size=2*d,\n"," num_attention_heads=h,\n"," num_hidden_layers=l)\n"," else:\n"," raise NotImplemented\n","\n","\n"," tokenizer = CharTokenizer()\n"," config.vocab_size = len(tokenizer.vocab)\n"," config.bos_token_id = tokenizer.bos_token_id\n"," config.eos_token_id = tokenizer.eos_token_id\n"," print(\"vocab size: \", len(tokenizer.vocab))\n"," print(\"new config: \", config)\n","\n"," v = config.vocab_size\n"," model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.bfloat16)\n"," print(\"embedding size: \", model.get_input_embeddings().weight.data.shape)\n"," else:\n"," print(\"Using pre-trained model weights...\")\n","\n"," tokenizer = CharTokenizer.from_pretrained(model_name_or_path)\n","\n"," model = transformers.AutoModelForCausalLM.from_pretrained(\n"," model_name_or_path\n"," )\n","\n"," if l is not None and h is not None and v is not None:\n"," print(\"theoretical # params: \", compute_llama_param(l, h, v))\n"," print(\"actual # params: \", count_params(model))\n","\n"," train_dataset.tokenizer = tokenizer\n","\n"," data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)\n","\n"," train_args = TrainingArguments(bf16=bf16, max_steps=1000,\n"," per_device_train_batch_size=32, eval_strategy=\"no\",\n"," save_steps=1000, save_total_limit=1, learning_rate=1e-4,\n"," weight_decay=0.0, warmup_ratio=0.2, lr_scheduler_type=\"cosine\",\n"," logging_steps=1, output_dir=output_dir, report_to=\"none\")\n","\n"," trainer = Trainer(model=model, tokenizer=tokenizer, args=train_args,\n"," train_dataset=train_dataset, data_collator=data_collator,\n"," eval_dataset=None)\n","\n"," if not random_initialize:\n"," print(\"resume training from: \", model_name_or_path)\n"," trainer.train(model_name_or_path)\n"," else:\n"," trainer.train()\n"," trainer.save_state()\n"," trainer.save_model(output_dir=output_dir)\n"," return model, tokenizer"],"metadata":{"id":"2PaLLB1bR5si","executionInfo":{"status":"ok","timestamp":1751815284880,"user_tz":-480,"elapsed":82,"user":{"displayName":"Xinyi Wang","userId":"00375459372959943015"}}},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":["Train a 2-layer language model on the generated synthetic graph:"],"metadata":{"id":"8NWaGVfcH0uu"}},{"cell_type":"code","source":["model, tokenizer = train(train_dataset)"],"metadata":{"collapsed":true,"id":"HJjCPy8zBts8","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"7eb080e0-5998-4976-9ea9-d01c5e815b87"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Random initializing...\n","vocab size: 20\n","new config: LlamaConfig {\n"," \"attention_bias\": false,\n"," \"attention_dropout\": 0.0,\n"," \"bos_token_id\": 16,\n"," \"eos_token_id\": 17,\n"," \"head_dim\": 64,\n"," \"hidden_act\": \"silu\",\n"," \"hidden_size\": 128,\n"," \"initializer_range\": 0.02,\n"," \"intermediate_size\": 256,\n"," \"max_position_embeddings\": 2048,\n"," \"mlp_bias\": false,\n"," \"model_type\": \"llama\",\n"," \"num_attention_heads\": 2,\n"," \"num_hidden_layers\": 2,\n"," \"num_key_value_heads\": 2,\n"," \"pretraining_tp\": 1,\n"," \"rms_norm_eps\": 1e-06,\n"," \"rope_scaling\": null,\n"," \"rope_theta\": 10000.0,\n"," \"tie_word_embeddings\": false,\n"," \"transformers_version\": \"4.53.0\",\n"," \"use_cache\": true,\n"," \"vocab_size\": 20\n","}\n","\n","embedding size: torch.Size([20, 128])\n","theoretical # params: 333440\n","actual # params: 333440\n"]},{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-10-3263160234.py:54: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(model=model, tokenizer=tokenizer, args=train_args,\n"]},{"output_type":"stream","name":"stdout","text":["None\n","worker id: \n","data buffer full\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","
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StepTraining Loss
12.836500
22.826400
32.829700
42.825400
52.838200
62.820100
72.816500
82.830600
92.830000
102.840600
112.831600
122.824400
132.828100
142.834000
152.813300
162.834300
172.837400
182.827100
192.827300
202.822400
212.811300
222.821900
232.811300
242.823000
252.813100
262.816900
272.816700
282.808200
292.804900
302.808900
312.827200
322.802400
332.797900
342.810200
352.793300
362.788600
372.793900
382.775100
392.793400
402.780700
412.784900
422.773300
432.777000
442.762100
452.753600
462.746100
472.734100
482.742400
492.748300
502.733800
512.727200
522.717200
532.712900
542.718900
552.714100
562.704200
572.682900
582.691800
592.688900
602.683000
612.666000
622.672000
632.657900
642.654100
652.637700
662.627200
672.621200
682.600700
692.582500
702.586700
712.584000
722.582000
732.561200
742.560000
752.551600
762.529400
772.525400
782.521700
792.495600
802.504500
812.490100
822.485100
832.456600
842.465300
852.440200
862.431400
872.418400
882.401300
892.398600
902.389600
912.389300
922.352600
932.368000
942.318600
952.346400
962.328100
972.291300
982.272200
992.255900
1002.262200
1012.233800
1022.238000
1032.202800
1042.201400
1052.177600
1062.166500
1072.181200
1082.164600
1092.181800
1102.151200
1112.143200
1122.090300
1132.089600
1142.088700
1152.111900
1162.084800
1172.030500
1182.044800
1192.056600
1202.040500
1212.029200
1222.005900
1231.997000
1242.006600
1251.965500
1261.983800
1271.972100
1281.956700
1291.926100
1301.946900
1311.911200
1321.930700
1331.904500
1341.923200
1351.868700
1361.915400
1371.860800
1381.848500
1391.883100
1401.867700
1411.823600
1421.855000
1431.813500
1441.816300
1451.821900
1461.813000
1471.811700
1481.794800
1491.769500
1501.808400
1511.786000
1521.797900
1531.792000
1541.768500
1551.772900
1561.790700
1571.754800
1581.763100
1591.765600
1601.756100
1611.744600
1621.722500
1631.735300
1641.740400
1651.722400
1661.712700
1671.709000
1681.721400
1691.722500
1701.701200
1711.715200
1721.703500
1731.696500
1741.699600
1751.677800
1761.672300
1771.713200
1781.702000
1791.695500
1801.701700
1811.688700
1821.685200
1831.668600
1841.679900
1851.655400
1861.651300
1871.673300
1881.686200
1891.650800
1901.687500
1911.634300
1921.665900
1931.670100
1941.643600
1951.641500
1961.634100
1971.650300
1981.643300
1991.629300
2001.667900
2011.662400
2021.623500
2031.641700
2041.623500
2051.617700
2061.625800
2071.660300
2081.620400
2091.615900
2101.612100
2111.618900
2121.604700
2131.624200
2141.642900
2151.620600
2161.573800
2171.608800
2181.632300
2191.620600
2201.615200
2211.589400
2221.591800
2231.629500
2241.637800
2251.608000
2261.627400
2271.605900
2281.616900
2291.620700
2301.634900
2311.622500
2321.622300
2331.567700
2341.632200
2351.615700
2361.613200
2371.597800
2381.636300
2391.589100
2401.619600
2411.588000
2421.588000
2431.592000
2441.548400
2451.607800
2461.614900
2471.579900
2481.564600
2491.581800
2501.572100
2511.572700
2521.574700
2531.582100
2541.583300
2551.593600
2561.554900
2571.570600
2581.595600
2591.558700
2601.551600
2611.548000
2621.547200
2631.576100
2641.551900
2651.539400
2661.541100
2671.568300
2681.542300
2691.568900
2701.561200
2711.540700
2721.558900
2731.536400
2741.548200
2751.547200
2761.527200
2771.538700
2781.534100
2791.531700
2801.539900
2811.537500
2821.544300
2831.519100
2841.524200
2851.527900
2861.527000
2871.560900
2881.547300
2891.545400
2901.527600
2911.543000
2921.538000
2931.510100
2941.520700
2951.501700
2961.524900
2971.531100
2981.531000
2991.534600
3001.529300
3011.513300
3021.504700
3031.546000
3041.519600
3051.516000
3061.503200
3071.534700
3081.522000
3091.496700
3101.499800
3111.503400
3121.513200
3131.501000
3141.523300
3151.515600
3161.505200
3171.499400
3181.471300
3191.473800
3201.525400
3211.508800
3221.504000
3231.505800
3241.501100
3251.489800
3261.495400
3271.501500
3281.512600
3291.503900
3301.491400
3311.487200
3321.487100
3331.477300
3341.507900
3351.479300
3361.445900
3371.523600
3381.463200
3391.484800
3401.460200
3411.460400
3421.461400

"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["None\n","worker id: \n","data buffer full\n"]}]},{"cell_type":"markdown","source":["Evaluation data class:"],"metadata":{"id":"bLkdCY9CHUvr"}},{"cell_type":"code","source":["class EvalDataset(Dataset):\n","\n"," def __init__(self,\n"," graph,\n"," tokenizer,\n"," split=\"id\", # or \"uniform\", or \"mem\"\n"," num_options=10,\n"," use_rule_length=False,\n"," seed=42):\n","\n"," super(EvalDataset, self).__init__()\n"," self.split = split\n"," self.tokenizer = tokenizer\n"," self.eos_token = self.tokenizer.eos_token\n"," self.num_options = num_options\n"," self.num_test = graph.num_test\n"," set_seed(seed)\n"," self.path_length = []\n","\n"," self.data = graph\n"," if split == 'id':\n"," self.triples = self.data.id_test_triples\n"," self.alt_ts = self.data.id_alt_ts\n"," elif split == 'uniform':\n"," self.triples = self.data.uniform_test_triples\n"," self.alt_ts = self.data.uniform_alt_ts\n"," elif split == \"mem\":\n"," self.triples = self.mem_triples\n"," else:\n"," print(\"no such split: \", split)\n"," raise NotImplementedError\n","\n"," if use_rule_length:\n"," print(\"using rule length\")\n"," self.path_length = [min([len(rule) - 1 for rule in rules]) for rules in self.data.test_rules]\n","\n"," self.get_data()\n"," if len(self.path_length) == 0:\n"," self.get_path_length()\n","\n"," def get_path_length(self):\n"," for h, r, t in self.input_triples:\n"," try:\n"," l = nx.shortest_path_length(self.data.G, source=h, target=t)\n"," except:\n"," l = 0\n"," print(f'cannot find shortest path between {h} and {t}')\n"," self.path_length.append(l)\n"," print(\"avg path length: \", np.mean(self.path_length))\n","\n"," def get_data(self):\n"," self.input_text = []\n"," self.input_triples = []\n"," self.seen_ts = []\n"," self.options = []\n","\n"," for idx, triple in enumerate(self.triples):\n"," h, r, t = triple\n","\n"," if self.split == \"mem\":\n"," seen_ts = []\n"," if h in self.data.G:\n"," for e in self.data.G[h]:\n"," if r in self.data.G[h][e]['id']:\n"," seen_ts.append(e)\n"," else:\n"," seen_ts = self.alt_ts[idx]\n"," self.seen_ts.append(seen_ts)\n","\n"," question = h + ' ' + r + ' '\n"," ans = t\n","\n"," options = [ans]\n"," for i in range(self.num_options-1):\n"," neg_e = random.choice(self.data.all_es)\n"," while neg_e == ans or neg_e in seen_ts:\n"," neg_e = random.choice(self.data.all_es)\n"," options.append(neg_e)\n","\n"," self.input_text.append(question)\n"," random.shuffle(options)\n"," self.options.append(options)\n","\n"," self.input_triples.append(triple)\n","\n"," def __len__(self):\n"," return len(self.input_text)\n","\n"," def __getitem__(self, i):\n"," example = [self.input_text[i], self.input_triples[i], self.seen_ts[i]]\n"," example += [self.options[i]]\n"," example.append(self.path_length[i])\n"," return example"],"metadata":{"id":"Tps9kqoIFuf8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Create a evaluation dataset based on the synthetic graph generated before:"],"metadata":{"id":"WlBrHd-iHZMd"}},{"cell_type":"code","source":["eval_dataset = EvalDataset(graph, tokenizer)"],"metadata":{"id":"ksgtknd-PpkC"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["The evaluation function (set device to \"cuda\" if you have GPUs):"],"metadata":{"id":"zp1GYdroJrho"}},{"cell_type":"code","source":["def eval(eval_dataset, model, batch_size=16, max_length=64, num_test=1000, device=\"cpu\"):\n","\n"," def forward(\n"," self,\n"," input_ids: torch.LongTensor = None,\n"," attention_mask: Optional[torch.Tensor] = None,\n"," position_ids: Optional[torch.LongTensor] = None,\n"," past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,\n"," inputs_embeds: Optional[torch.FloatTensor] = None,\n"," labels: Optional[torch.LongTensor] = None,\n"," use_cache: Optional[bool] = None,\n"," output_attentions: Optional[bool] = None,\n"," output_hidden_states: Optional[bool] = True,\n"," return_dict: Optional[bool] = None,\n"," cache_position: Optional[torch.LongTensor] = None,\n"," ) -> Union[Tuple, CausalLMOutputWithPast]:\n"," r\"\"\"\n"," Args:\n"," labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n"," Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,\n"," config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored\n"," (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.\n","\n"," Returns:\n","\n"," Example:\n","\n"," ```python\n"," >>> from transformers import AutoTokenizer, LlamaForCausalLM\n","\n"," >>> model = LlamaForCausalLM.from_pretrained(\"meta-llama/Llama-2-7b-hf\")\n"," >>> tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Llama-2-7b-hf\")\n","\n"," >>> prompt = \"Hey, are you conscious? Can you talk to me?\"\n"," >>> inputs = tokenizer(prompt, return_tensors=\"pt\")\n","\n"," >>> # Generate\n"," >>> generate_ids = model.generate(inputs.input_ids, max_length=30)\n"," >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n"," \"Hey, are you conscious? Can you talk to me?\\nI'm not conscious, but I can talk to you.\"\n"," ```\"\"\"\n"," output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n"," output_hidden_states = (\n"," output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n"," )\n"," return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n","\n"," # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\n"," outputs = self.model(\n"," input_ids=input_ids,\n"," attention_mask=attention_mask,\n"," position_ids=position_ids,\n"," past_key_values=past_key_values,\n"," inputs_embeds=inputs_embeds,\n"," use_cache=use_cache,\n"," output_attentions=output_attentions,\n"," output_hidden_states=output_hidden_states,\n"," return_dict=return_dict,\n"," cache_position=cache_position,\n"," )\n","\n"," hidden_states = outputs[0]\n"," # hidden_states = outputs.hidden_states[-2]\n"," logits = self.lm_head(hidden_states)\n"," logits = logits.float()\n","\n"," loss = None\n"," if labels is not None:\n"," # Shift so that tokens < n predict n\n"," shift_logits = logits[..., :-1, :].contiguous()\n"," shift_labels = labels[..., 1:].contiguous()\n"," # Flatten the tokens\n"," loss_fct = CrossEntropyLoss(reduction='none')\n"," shift_logits = shift_logits.view(-1, self.config.vocab_size)\n"," shift_labels = shift_labels.view(-1)\n"," # Enable model parallelism\n"," shift_labels = shift_labels.to(shift_logits.device)\n"," loss = loss_fct(shift_logits, shift_labels)\n"," loss = loss.view([labels.size(0), labels.size(1) - 1])\n"," loss = loss.sum(-1)\n","\n"," if not return_dict:\n"," output = (logits,) + outputs[1:]\n"," return (loss,) + output if loss is not None else output\n","\n"," return CausalLMOutputWithPast(\n"," loss=loss,\n"," logits=logits,\n"," past_key_values=outputs.past_key_values,\n"," hidden_states=outputs.hidden_states,\n"," attentions=outputs.attentions,\n"," )\n","\n"," # monkey patching\n"," original_forward = LlamaForCausalLM.forward\n"," LlamaForCausalLM.forward = forward\n"," model.eval()\n","\n"," def collect_data(instances, device='cpu'):\n"," input_ids = instances[\"input_ids\"]\n"," labels = instances[\"labels\"]\n"," input_ids = torch.nn.utils.rnn.pad_sequence(\n"," input_ids, batch_first=True, padding_value=tokenizer.pad_token_id\n"," )\n"," labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True,\n"," padding_value=IGNORE_INDEX)\n","\n"," attn_mask = input_ids.ne(tokenizer.pad_token_id)\n","\n"," return dict(\n"," input_ids=input_ids.to(device),\n"," labels=labels.to(device),\n"," attention_mask=attn_mask.to(device),\n"," )\n","\n"," num_choices = eval_dataset.num_options\n"," print(\"number of choices: \", num_choices)\n","\n"," input_texts = []\n"," output_texts = []\n"," gts = []\n"," example_ids = []\n"," id = 0\n"," num_correct = 0\n"," num_all = 0\n"," losses = []\n","\n"," for q, triple, seen_t, opts, l in eval_dataset:\n"," id += 1\n"," if id > min(num_test, eval_dataset.num_test):\n"," break\n"," print(q, triple, seen_t, opts, l)\n"," label = triple[-1]\n"," input_text = q\n"," for op in opts:\n"," input_texts.append(input_text)\n"," output_texts.append(op)\n"," gts.append(op == label)\n"," example_ids.append(id)\n","\n"," if len(input_texts) >= batch_size or id == min(len(eval_dataset)-1, num_test):\n"," data_dict = prepare_data(input_texts, output_texts, tokenizer, max_length)\n"," input_data = collect_data(data_dict, device)\n"," print(\"input data shape: \", input_data['input_ids'].shape)\n"," loss = model(**input_data).loss.detach().cpu()\n"," print(\"loss: \", loss)\n"," for i in range(len(input_texts)//num_choices):\n"," pred = torch.argmin(loss[i*num_choices: (i+1)*num_choices]).item()\n"," gt = np.arange(num_choices)[gts[i*num_choices: (i+1)*num_choices]][0]\n"," losses.append(loss[i*num_choices: (i+1)*num_choices][gt])\n"," if pred == gt:\n"," num_correct += 1\n"," num_all += 1\n","\n"," acc = num_correct/num_all\n"," print(\"Accuracy: \", acc)\n"," mean_loss = np.mean(losses)\n"," print(\"Loss: \", mean_loss)\n","\n"," input_texts = []\n"," output_texts = []\n"," gts = []\n"," example_ids = []\n","\n"," # restore the original forward function\n"," LlamaForCausalLM.forward = original_forward\n","\n"," return acc, mean_loss"],"metadata":{"id":"7bJvX4JlJWA0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Evaluate the previously trained language model:"],"metadata":{"id":"VQRbkUPfOj46"}},{"cell_type":"code","source":["acc, loss = eval(eval_dataset, model)"],"metadata":{"id":"lWS9yJEaHTBO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Sweeping function to train and evaluate a range of model sizes and plot the U-shaped loss curve and acc curve (this will likely take a very long time/crush with only Colab CPU):"],"metadata":{"id":"D_FCtkTJPI7e"}},{"cell_type":"code","source":["def scaling_sweep(train_dataset, eval_dataset, y_axis='loss'):\n"," accs = []\n"," losses = []\n"," error_losses = []\n"," sizes = []\n"," for i in range(1, 4):\n"," for j in [i-1, i, i+1]:\n"," if j > 0 and j < 4:\n"," num_p = compute_llama_param(2**i, 2**j, 19)\n"," model, tokenizer = train(train_dataset, f'llama-{2**i}-{2**j}')\n"," acc, loss = eval(eval_dataset, model)\n"," accs.append(acc)\n"," losses.append(loss)\n"," sizes.append(num_p)\n","\n"," idx = np.argsort(sizes)\n"," accs = np.array(accs)[idx]\n"," losses = np.array(losses)[idx]\n"," sizes = np.array(sizes)[idx]\n"," log_sizes = np.log(sizes)\n"," print('log sizes: ', log_sizes)\n"," print('accs: ', accs)\n"," print('losses: ', losses)\n","\n"," # plot acc\n"," plt.plot(log_sizes, accs, 'o-', linewidth=2)\n"," plt.xticks(np.log(sizes), (sizes/1000000).round(1), size=8)\n"," plt.xlabel(f'Llama model size (M)', size=12)\n"," plt.ylabel(f'Accuracy', size=12)\n"," plt.legend(fontsize=12)\n"," plt.show()\n"," plt.savefig('acc.png')\n"," plt.close()\n","\n"," # plot loss\n"," plt.plot(log_sizes, losses, 'o-', linewidth=2)\n"," plt.xticks(np.log(sizes), (sizes/1000000).round(1), size=8)\n"," plt.xlabel(f'Llama model size (M)', size=12)\n"," plt.ylabel(f'Loss', size=12)\n"," plt.legend(loc='upper left', fontsize=12)\n"," plt.show()\n"," plt.savefig('loss.png')\n"," plt.close()"],"metadata":{"id":"pDvf2laCOiLW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["scaling_sweep(train_dataset, eval_dataset)"],"metadata":{"id":"l7h6QycrSe82"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0}