+1 -1

.gitignore

··· 1 1 .env 2 2 **/__pycache__/** 3 3 data/* 4 4 - .vscode 4 4 + .vscode

+1

data/training/.gitignore

··· 1 1 + ./*

+1

pyproject.toml

··· 13 13 "rich>=14.1.0", 14 14 "ruff>=0.13.2", 15 15 "torch>=2.8.0", 16 16 + "torchdata>=0.11.0", 16 17 ]

scripts/__init__.py

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scripts/split_follows.py

··· 6 6 import random 7 7 import sys 8 8 9 9 - import pandas as pd 9 9 + import numpy as np 10 10 11 11 12 12 logger = logging.getLogger(__name__) ··· 37 37 val_file_idx = 1 38 38 train_follows: list[tuple[int, int]] = [] 39 39 val_follows: list[tuple[int, int]] = [] 40 40 + train_files = [] 41 41 + val_files = [] 40 42 for file in os.listdir(follow_dir): 41 43 source_did = file[:-3] # Remove .gz extension 42 44 if source_did not in did_id_map: ··· 50 52 if random.random() < val_split: 51 53 val_follows.append((did_id_map[source_did], did_id_map[target_did])) 52 54 if len(val_follows) >= file_size: 53 53 - logger.info(f"Saving validation file: {val_file_idx}") 54 54 - val_df = pd.DataFrame( 55 55 - val_follows, columns=["source", "target"], index=None 56 56 - ) 57 57 - val_df.to_parquet( 58 58 - os.path.join(output_dir, f"val_follows_{val_file_idx}.parquet") 55 55 + filename = f"val_follows_{val_file_idx}.data" 56 56 + logger.info(f"Saving validation file: {filename}") 57 57 + val_df = np.array(val_follows, dtype="int64") 58 58 + val_file_df = np.memmap( 59 59 + os.path.join(output_dir, filename), 60 60 + dtype="int64", 61 61 + mode="w+", 62 62 + shape=val_df.shape, 59 63 ) 64 64 + val_file_df[:] = val_df[:] 65 65 + val_file_df.flush() 60 66 val_follows = [] 67 67 + val_files.append( 68 68 + { 69 69 + "filename": filename, 70 70 + "shape": val_df.shape, 71 71 + "dtype": "int64", 72 72 + } 73 73 + ) 61 74 val_file_idx += 1 62 75 del val_df 76 76 + del val_file_df 63 77 else: 64 78 train_follows.append((did_id_map[source_did], did_id_map[target_did])) 65 79 if len(train_follows) >= file_size: 66 66 - logger.info(f"Saving training file: {train_file_idx}") 67 67 - train_df = pd.DataFrame( 68 68 - train_follows, columns=["source", "target"], index=None 69 69 - ) 70 70 - train_df.to_parquet( 71 71 - os.path.join( 72 72 - output_dir, f"train_follows_{train_file_idx}.parquet" 73 73 - ) 80 80 + filename = f"train_follows_{train_file_idx}.data" 81 81 + logger.info(f"Saving training file: {filename}") 82 82 + train_df = np.array(train_follows, dtype="int64") 83 83 + train_file_df = np.memmap( 84 84 + os.path.join(output_dir, filename), 85 85 + dtype="int64", 86 86 + mode="w+", 87 87 + shape=train_df.shape, 74 88 ) 89 89 + train_file_df[:] = train_df[:] 90 90 + train_file_df.flush() 75 91 train_follows = [] 92 92 + train_files.append( 93 93 + { 94 94 + "filename": filename, 95 95 + "shape": train_df.shape, 96 96 + "dtype": "int64", 97 97 + } 98 98 + ) 76 99 train_file_idx += 1 77 100 del train_df 101 101 + del train_file_df 78 102 79 103 logger.info("Saving remnant files...") 80 104 if len(val_follows): 81 81 - val_df = pd.DataFrame(val_follows, columns=["source", "target"], index=None) 82 82 - val_df.to_parquet( 83 83 - os.path.join(output_dir, f"val_follows_{val_file_idx}.parquet") 105 105 + filename = f"val_follows_{val_file_idx}.data" 106 106 + val_df = np.array(val_follows, dtype="int64") 107 107 + val_file_df = np.memmap( 108 108 + os.path.join(output_dir, filename), 109 109 + dtype="int64", 110 110 + mode="w+", 111 111 + shape=val_df.shape, 112 112 + ) 113 113 + val_file_df[:] = val_df[:] 114 114 + val_file_df.flush() 115 115 + val_files.append( 116 116 + { 117 117 + "filename": filename, 118 118 + "shape": val_df.shape, 119 119 + "dtype": "int64", 120 120 + } 84 121 ) 85 122 86 123 if len(train_follows): 87 87 - train_df = pd.DataFrame(train_follows, columns=["source", "target"], index=None) 88 88 - train_df.to_parquet( 89 89 - os.path.join(output_dir, f"train_follows_{train_file_idx}.parquet") 124 124 + filename = f"train_follows_{train_file_idx}.data" 125 125 + train_df = np.array(train_follows, dtype="int64") 126 126 + train_file_df = np.memmap( 127 127 + os.path.join(output_dir, filename), 128 128 + dtype="int64", 129 129 + mode="w+", 130 130 + shape=train_df.shape, 131 131 + ) 132 132 + train_file_df[:] = train_df[:] 133 133 + train_file_df.flush() 134 134 + train_files.append( 135 135 + { 136 136 + "filename": filename, 137 137 + "shape": train_df.shape, 138 138 + "dtype": "int64", 139 139 + } 90 140 ) 91 141 92 142 logger.info("Finished reading follows") ··· 95 145 logger.info("Writing id map...") 96 146 with open(os.path.join(output_dir, "did_id_map.json"), "w") as out_file: 97 147 out_file.write(json.dumps(did_id_map)) 148 148 + 149 149 + # Write out file metadata 150 150 + logger.info("Writing dataset metadata") 151 151 + with open(os.path.join(output_dir, "metadata.json"), "w") as out_file: 152 152 + out_file.write( 153 153 + json.dumps( 154 154 + { 155 155 + "did_id_map": "did_id_map.json", 156 156 + "splits": {"training": train_files, "validation": val_files}, 157 157 + } 158 158 + ) 159 159 + ) 98 160 99 161 100 162 if __name__ == "__main__":

scripts/training/__init__.py

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scripts/training/data.py

··· 1 1 import json 2 2 + import glob 3 3 + import os 2 4 import random 3 5 4 4 - import polars as pl 5 5 - from torch import Dataset 6 6 + import numpy as np 7 7 + import torch 8 8 + from torch.utils.data import Dataset 6 9 7 10 8 11 class FollowDataset(Dataset): 9 12 def __init__( 10 10 - self, did_id_map_path: str, parquet_file_path, negative_sample_count: int 13 13 + self, 14 14 + dataset_path: str, 15 15 + split: str, 16 16 + negative_sample_count: int, 11 17 ): 12 12 - with open(did_id_map_path, "r") as in_file: 18 18 + with open(os.path.join(dataset_path, "metadata.json"), "r") as in_file: 19 19 + metadata = json.load(in_file) 20 20 + 21 21 + with open(os.path.join(dataset_path, metadata["did_id_map"]), "r") as in_file: 13 22 self.did_id_map: dict[str, int] = json.load(in_file) 14 14 - self.id_arr: list[int] = list(self.did_id_map.keys()) 15 23 16 16 - self.follows = pl.read_parquet(parquet_file_path) 17 17 - self.follow_map: dict[int, list[int]] = {} 18 18 - for row in self.follows.rows(): 19 19 - if row[0] not in self.follow_map: 20 20 - self.follow_map[row[0]] = [] 21 21 - self.follow_map[row[0]].append(row[1]) 24 24 + if split not in metadata["splits"]: 25 25 + raise ValueError(f"Could not find split in metadata file: {split}") 26 26 + split_files = metadata["splits"][split] 27 27 + 28 28 + self.parquet_files: list[tuple[str, str, int, int]] = [] 29 29 + for file in split_files: 30 30 + file_idx = file["filename"].split("_")[2].split(".")[0] 31 31 + self.parquet_files.append((file["filename"], file["dtype"], file_idx, file["shape"][0])) # type: ignore 32 32 + 33 33 + self.parquet_files.sort(key=lambda x: x[1]) 34 34 + self.dataframes: list[np.ndarray] = [] 35 35 + for filename, dtype, _, row_count in self.parquet_files: 36 36 + self.dataframes.append( 37 37 + np.memmap( 38 38 + os.path.join(dataset_path, filename), 39 39 + dtype=dtype, 40 40 + mode="r", 41 41 + shape=(row_count, 2), 42 42 + ) 43 43 + ) 22 44 23 45 self.negative_sample_count = negative_sample_count 24 46 25 25 - def __len__(self): 26 26 - return self.follows.shape[0] 47 47 + def __len__(self) -> int: 48 48 + return sum((row_count for _, _, _, row_count in self.parquet_files)) 49 49 + 50 50 + def num_users(self) -> int: 51 51 + return len(self.did_id_map) 52 52 + 53 53 + def _idx_to_row(self, idx: int) -> tuple[int, int]: 54 54 + if idx < 0 or idx >= len(self): 55 55 + raise IndexError(f"Invalid index: {idx}") 56 56 + 57 57 + # Find which file contains index 58 58 + row_index_total = 0 59 59 + effective_idx = idx 60 60 + i = 0 61 61 + for i, (_, _, _, row_count) in enumerate(self.parquet_files): 62 62 + row_index_total += row_count 63 63 + if idx < row_index_total: 64 64 + break 65 65 + effective_idx -= row_count 27 66 28 28 - def __get_item__(self, idx: int) -> list[tuple[int, int]]: 67 67 + row = self.dataframes[i][effective_idx] 68 68 + return (row[0].item(), row[1].item()) 69 69 + 70 70 + def __getitem__(self, idx: int) -> tuple[list[tuple[int, int]], list[int]]: 29 71 """ 30 72 Grab follow connection and create negative samples (follows that dont exist) 31 73 """ 32 32 - sample = self.follows.item(idx) 33 33 - negative_samples = [] 74 74 + samples = [self._idx_to_row(idx)] 75 75 + 34 76 for _ in range(self.negative_sample_count): 35 35 - attempt = 0 36 36 - while attempt < 5: 37 37 - rand_idx = random.randint(0, len(self.id_arr) - 1) 38 38 - rand_user = self.id_arr[rand_idx] 39 39 - if rand_user not in self.follow_map[sample[0]]: 40 40 - break 41 41 - attempt += 1 42 42 - if attempt == 5: 43 43 - raise Exception(f"Failed to find negative sample for id: {sample[0]}") 44 44 - negative_samples.append((sample[0], rand_user)) 77 77 + rand_idx = random.randrange(0, len(self.did_id_map)) 78 78 + # Assume that users arent connected (roughly 1/700 chance they are) 79 79 + samples.append((samples[0][0], rand_idx)) 45 80 46 46 - return [(sample[0], sample[1])] + negative_samples 81 81 + labels = [-1] * len(samples) 82 82 + labels[0] = 1 83 83 + return samples, labels 84 84 + 85 85 + @staticmethod 86 86 + def collate_rows( 87 87 + batch: list[tuple[list[tuple[int, int]], list[int]]], 88 88 + ) -> tuple[torch.Tensor, torch.Tensor]: 89 89 + follows = torch.concat(tuple(torch.IntTensor(follow) for follow, _ in batch)) 90 90 + labels = torch.concat(tuple(torch.IntTensor(label) for _, label in batch)) 91 91 + return (follows, labels)

+47 -14

scripts/training/models.py

··· 1 1 import torch 2 2 from torch import nn 3 3 + from torch.utils.data import DataLoader 4 4 + import torch.nn.functional as F 5 5 + import lightning as pyl 3 6 7 7 + from scripts.training.data import FollowDataset 4 8 5 5 - class FollowEmbed(nn.Module): 9 9 + 10 10 + class UserEmbedding(nn.Module): 6 11 def __init__(self, num_embeds: int, size: int): 7 12 super().__init__() 8 8 - self.source_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=size) 9 9 - self.target_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=size) 10 10 - self.decision_layer = nn.Linear(1, 1) 13 13 + self.embed = nn.Embedding( 14 14 + num_embeddings=num_embeds, embedding_dim=size, dtype=torch.bfloat16 15 15 + ) 11 16 12 17 def forward(self, batch): 13 13 - source: torch.tensor = self.source_embed(batch[:, 0]) 14 14 - target: torch.tensor = self.target_embed(batch[:, 1]) 15 15 - return torch.bmm(source.unsqueeze(1), target.unsqueeze(2)).squeeze() 18 18 + return self.embed(batch) 19 19 + 20 20 + 21 21 + class FollowEmbedModule(pyl.LightningModule): 22 22 + def __init__(self, source_embedding, target_embedding): 23 23 + super().__init__() 24 24 + self.source_embed = source_embedding 25 25 + self.target_embed = target_embedding 26 26 + 27 27 + def training_step(self, batch, _): 28 28 + x, y = batch 29 29 + rotated_x = x.view(x.size(0), -1) 30 30 + source = self.source_embed(rotated_x[:, 0]) 31 31 + target: torch.Tensor = self.target_embed(rotated_x[:, 1]) 32 32 + dot = torch.bmm(source.unsqueeze(1), target.unsqueeze(2)).squeeze() 33 33 + loss = -1 * torch.mean(torch.log(F.sigmoid(dot * y))) 34 34 + self.log("train_loss", loss, prog_bar=False) 35 35 + return loss 16 36 37 37 + def validation_step(self, batch, _): 38 38 + x, y = batch 39 39 + rotated_x = x.view(x.size(0), -1) 40 40 + source = self.source_embed(rotated_x[:, 0]) 41 41 + target: torch.Tensor = self.target_embed(rotated_x[:, 1]) 42 42 + dot = torch.bmm(source.unsqueeze(1), target.unsqueeze(2)).squeeze() 43 43 + loss = -1 * torch.mean(torch.log(F.sigmoid(dot * y))) 44 44 + self.log("val_loss", loss, prog_bar=False) 45 45 + return loss 17 46 18 18 - if __name__ == "__main__": 19 19 - embeds = FollowEmbed(10, 4) 20 20 - x = torch.tensor([[1, 2], [3, 4], [5, 6]]) 21 21 - print(f"Input tensor: {x.shape}") 22 22 - y = embeds(x) 23 23 - print(f"Output shape: {y.shape}") 24 24 - print(y) 47 47 + def on_validation_epoch_end(self): 48 48 + avg_loss = self.trainer.callback_metrics.get("val_loss") 49 49 + if avg_loss is not None: 50 50 + print(f"\nValidation Loss: {avg_loss:.4f}\n") 51 51 + avg_loss = self.trainer.callback_metrics.get("train_loss") 52 52 + if avg_loss is not None: 53 53 + print(f"\nTraining Loss: {avg_loss:.4f}\n") 54 54 + 55 55 + def configure_optimizers(self): 56 56 + optimizers = torch.optim.Adagrad(self.parameters(), lr=1e-1) 57 57 + return optimizers

+113

scripts/training/train.py

··· 1 1 + from argparse import ArgumentParser 2 2 + from datetime import datetime 3 3 + import os 4 4 + from typing import Optional 5 5 + 6 6 + import lightning as pyl 7 7 + from lightning.pytorch.loggers import WandbLogger 8 8 + from lightning.pytorch.callbacks import ModelCheckpoint 9 9 + import torch 10 10 + from torchdata.stateful_dataloader import StatefulDataLoader 11 11 + 12 12 + from scripts.training.data import FollowDataset 13 13 + from scripts.training.models import UserEmbedding, FollowEmbedModule 14 14 + 15 15 + 16 16 + PROJECT = "goodposts-followers" 17 17 + SAVE_DIR = "./data/training" 18 18 + NAME = "following-embedding" 19 19 + 20 20 + 21 21 + def main(run_id: Optional[str], model_checkpoint: Optional[str]): 22 22 + if run_id is None: 23 23 + run_id = NAME + str(int(datetime.now().timestamp())) 24 24 + 25 25 + print("Loading validation dataset") 26 26 + val_dataset = FollowDataset( 27 27 + dataset_path="data/processed/numpy/", 28 28 + split="validation", 29 29 + negative_sample_count=0, 30 30 + ) 31 31 + 32 32 + print("Loading training dataset") 33 33 + train_dataset = FollowDataset( 34 34 + dataset_path="data/processed/numpy/", 35 35 + split="training", 36 36 + negative_sample_count=2, 37 37 + ) 38 38 + 39 39 + val_dataloader = StatefulDataLoader( 40 40 + val_dataset, 41 41 + pin_memory=True, 42 42 + collate_fn=val_dataset.collate_rows, 43 43 + batch_size=1024, 44 44 + num_workers=7, 45 45 + ) 46 46 + 47 47 + train_dataloader = StatefulDataLoader( 48 48 + train_dataset, 49 49 + pin_memory=True, 50 50 + collate_fn=train_dataset.collate_rows, 51 51 + batch_size=1024, 52 52 + num_workers=7, 53 53 + ) 54 54 + 55 55 + print("Instantiating embedding model") 56 56 + wandb_logger = WandbLogger( 57 57 + project=PROJECT, 58 58 + save_dir=SAVE_DIR, 59 59 + name=NAME, 60 60 + id=run_id, 61 61 + resume="allow", 62 62 + ) 63 63 + 64 64 + checkpoint_callback = ModelCheckpoint( 65 65 + dirpath=os.path.join(SAVE_DIR, "checkpoints", run_id), 66 66 + filename="{epoch}-{step}", 67 67 + save_last=True, 68 68 + save_top_k=1, 69 69 + monitor="val_loss", 70 70 + ) 71 71 + 72 72 + embedding = FollowEmbedModule( 73 73 + source_embedding=UserEmbedding(train_dataset.num_users(), 64), 74 74 + target_embedding=UserEmbedding(train_dataset.num_users(), 64), 75 75 + ) 76 76 + 77 77 + torch.set_float32_matmul_precision("medium") 78 78 + trainer = pyl.Trainer( 79 79 + logger=wandb_logger, 80 80 + callbacks=[checkpoint_callback], 81 81 + accelerator="gpu", 82 82 + devices=1, 83 83 + precision="bf16", 84 84 + max_epochs=2, 85 85 + limit_train_batches=0.01, 86 86 + limit_val_batches=1_000, 87 87 + val_check_interval=0.05, 88 88 + ) 89 89 + trainer.fit( 90 90 + model=embedding, 91 91 + train_dataloaders=train_dataloader, 92 92 + val_dataloaders=val_dataloader, 93 93 + ckpt_path=model_checkpoint, 94 94 + ) 95 95 + 96 96 + 97 97 + if __name__ == "__main__": 98 98 + parser = ArgumentParser() 99 99 + parser.add_argument( 100 100 + "-r", 101 101 + "--run-id", 102 102 + dest="run_id", 103 103 + help="ID for training run", 104 104 + ) 105 105 + parser.add_argument( 106 106 + "-m", 107 107 + "--model-checkpoint", 108 108 + dest="model_checkpoint", 109 109 + help="Path to checkpoint with run data", 110 110 + ) 111 111 + 112 112 + args = parser.parse_args() 113 113 + main(run_id=args.run_id, model_checkpoint=args.model_checkpoint)

+114

uv.lock

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