this repo has no description
Switch to memory mapped dataset and get training scripts set up
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data/training/.gitignore
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data/training/.gitignore
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./*
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pyproject.toml
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pyproject.toml
scripts/__init__.py
scripts/__init__.py
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scripts/split_follows.py
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scripts/split_follows.py
···
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import random
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import sys
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import pandas as pd
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import numpy as np
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logger = logging.getLogger(__name__)
···
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val_file_idx = 1
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train_follows: list[tuple[int, int]] = []
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val_follows: list[tuple[int, int]] = []
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train_files = []
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val_files = []
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for file in os.listdir(follow_dir):
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source_did = file[:-3] # Remove .gz extension
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if source_did not in did_id_map:
···
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if random.random() < val_split:
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val_follows.append((did_id_map[source_did], did_id_map[target_did]))
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if len(val_follows) >= file_size:
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logger.info(f"Saving validation file: {val_file_idx}")
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val_df = pd.DataFrame(
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val_follows, columns=["source", "target"], index=None
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)
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val_df.to_parquet(
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os.path.join(output_dir, f"val_follows_{val_file_idx}.parquet")
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filename = f"val_follows_{val_file_idx}.data"
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logger.info(f"Saving validation file: {filename}")
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val_df = np.array(val_follows, dtype="int64")
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val_file_df = np.memmap(
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os.path.join(output_dir, filename),
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dtype="int64",
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mode="w+",
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shape=val_df.shape,
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)
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val_file_df[:] = val_df[:]
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val_file_df.flush()
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val_follows = []
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val_files.append(
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{
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"filename": filename,
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"shape": val_df.shape,
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"dtype": "int64",
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}
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)
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val_file_idx += 1
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del val_df
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del val_file_df
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else:
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train_follows.append((did_id_map[source_did], did_id_map[target_did]))
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if len(train_follows) >= file_size:
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logger.info(f"Saving training file: {train_file_idx}")
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train_df = pd.DataFrame(
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train_follows, columns=["source", "target"], index=None
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)
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train_df.to_parquet(
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os.path.join(
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output_dir, f"train_follows_{train_file_idx}.parquet"
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)
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filename = f"train_follows_{train_file_idx}.data"
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logger.info(f"Saving training file: {filename}")
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train_df = np.array(train_follows, dtype="int64")
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train_file_df = np.memmap(
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os.path.join(output_dir, filename),
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dtype="int64",
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mode="w+",
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shape=train_df.shape,
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)
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train_file_df[:] = train_df[:]
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train_file_df.flush()
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train_follows = []
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train_files.append(
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{
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"filename": filename,
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"shape": train_df.shape,
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"dtype": "int64",
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}
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)
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train_file_idx += 1
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del train_df
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del train_file_df
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logger.info("Saving remnant files...")
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if len(val_follows):
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val_df = pd.DataFrame(val_follows, columns=["source", "target"], index=None)
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val_df.to_parquet(
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os.path.join(output_dir, f"val_follows_{val_file_idx}.parquet")
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filename = f"val_follows_{val_file_idx}.data"
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val_df = np.array(val_follows, dtype="int64")
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val_file_df = np.memmap(
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os.path.join(output_dir, filename),
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dtype="int64",
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mode="w+",
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shape=val_df.shape,
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)
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val_file_df[:] = val_df[:]
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val_file_df.flush()
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val_files.append(
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{
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"filename": filename,
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"shape": val_df.shape,
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"dtype": "int64",
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}
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)
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if len(train_follows):
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train_df = pd.DataFrame(train_follows, columns=["source", "target"], index=None)
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train_df.to_parquet(
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os.path.join(output_dir, f"train_follows_{train_file_idx}.parquet")
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filename = f"train_follows_{train_file_idx}.data"
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train_df = np.array(train_follows, dtype="int64")
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train_file_df = np.memmap(
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os.path.join(output_dir, filename),
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dtype="int64",
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mode="w+",
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shape=train_df.shape,
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)
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train_file_df[:] = train_df[:]
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train_file_df.flush()
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train_files.append(
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{
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"filename": filename,
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"shape": train_df.shape,
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"dtype": "int64",
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}
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)
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logger.info("Finished reading follows")
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logger.info("Writing id map...")
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with open(os.path.join(output_dir, "did_id_map.json"), "w") as out_file:
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out_file.write(json.dumps(did_id_map))
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# Write out file metadata
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logger.info("Writing dataset metadata")
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with open(os.path.join(output_dir, "metadata.json"), "w") as out_file:
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out_file.write(
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json.dumps(
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{
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"did_id_map": "did_id_map.json",
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"splits": {"training": train_files, "validation": val_files},
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}
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)
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)
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if __name__ == "__main__":
scripts/training/__init__.py
scripts/training/__init__.py
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scripts/training/data.py
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scripts/training/data.py
···
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import json
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import glob
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import os
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import random
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import polars as pl
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from torch import Dataset
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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class FollowDataset(Dataset):
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def __init__(
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self, did_id_map_path: str, parquet_file_path, negative_sample_count: int
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self,
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dataset_path: str,
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split: str,
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negative_sample_count: int,
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):
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with open(did_id_map_path, "r") as in_file:
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with open(os.path.join(dataset_path, "metadata.json"), "r") as in_file:
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metadata = json.load(in_file)
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with open(os.path.join(dataset_path, metadata["did_id_map"]), "r") as in_file:
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self.did_id_map: dict[str, int] = json.load(in_file)
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self.id_arr: list[int] = list(self.did_id_map.keys())
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self.follows = pl.read_parquet(parquet_file_path)
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self.follow_map: dict[int, list[int]] = {}
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for row in self.follows.rows():
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if row[0] not in self.follow_map:
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self.follow_map[row[0]] = []
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self.follow_map[row[0]].append(row[1])
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if split not in metadata["splits"]:
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raise ValueError(f"Could not find split in metadata file: {split}")
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split_files = metadata["splits"][split]
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self.parquet_files: list[tuple[str, str, int, int]] = []
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for file in split_files:
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file_idx = file["filename"].split("_")[2].split(".")[0]
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self.parquet_files.append((file["filename"], file["dtype"], file_idx, file["shape"][0])) # type: ignore
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self.parquet_files.sort(key=lambda x: x[1])
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self.dataframes: list[np.ndarray] = []
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for filename, dtype, _, row_count in self.parquet_files:
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self.dataframes.append(
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np.memmap(
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os.path.join(dataset_path, filename),
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dtype=dtype,
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mode="r",
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shape=(row_count, 2),
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)
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)
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self.negative_sample_count = negative_sample_count
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def __len__(self):
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return self.follows.shape[0]
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def __len__(self) -> int:
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return sum((row_count for _, _, _, row_count in self.parquet_files))
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def num_users(self) -> int:
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return len(self.did_id_map)
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def _idx_to_row(self, idx: int) -> tuple[int, int]:
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if idx < 0 or idx >= len(self):
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raise IndexError(f"Invalid index: {idx}")
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# Find which file contains index
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row_index_total = 0
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effective_idx = idx
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i = 0
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for i, (_, _, _, row_count) in enumerate(self.parquet_files):
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row_index_total += row_count
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if idx < row_index_total:
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break
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effective_idx -= row_count
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def __get_item__(self, idx: int) -> list[tuple[int, int]]:
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row = self.dataframes[i][effective_idx]
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return (row[0].item(), row[1].item())
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def __getitem__(self, idx: int) -> tuple[list[tuple[int, int]], list[int]]:
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"""
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Grab follow connection and create negative samples (follows that dont exist)
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"""
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sample = self.follows.item(idx)
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negative_samples = []
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samples = [self._idx_to_row(idx)]
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for _ in range(self.negative_sample_count):
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attempt = 0
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while attempt < 5:
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rand_idx = random.randint(0, len(self.id_arr) - 1)
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rand_user = self.id_arr[rand_idx]
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if rand_user not in self.follow_map[sample[0]]:
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break
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attempt += 1
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if attempt == 5:
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raise Exception(f"Failed to find negative sample for id: {sample[0]}")
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negative_samples.append((sample[0], rand_user))
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rand_idx = random.randrange(0, len(self.did_id_map))
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# Assume that users arent connected (roughly 1/700 chance they are)
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samples.append((samples[0][0], rand_idx))
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return [(sample[0], sample[1])] + negative_samples
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labels = [-1] * len(samples)
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labels[0] = 1
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return samples, labels
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@staticmethod
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def collate_rows(
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batch: list[tuple[list[tuple[int, int]], list[int]]],
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) -> tuple[torch.Tensor, torch.Tensor]:
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follows = torch.concat(tuple(torch.IntTensor(follow) for follow, _ in batch))
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labels = torch.concat(tuple(torch.IntTensor(label) for _, label in batch))
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return (follows, labels)
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scripts/training/models.py
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scripts/training/models.py
···
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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import torch.nn.functional as F
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import lightning as pyl
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from scripts.training.data import FollowDataset
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class FollowEmbed(nn.Module):
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class UserEmbedding(nn.Module):
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def __init__(self, num_embeds: int, size: int):
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super().__init__()
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self.source_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=size)
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self.target_embed = nn.Embedding(num_embeddings=num_embeds, embedding_dim=size)
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self.decision_layer = nn.Linear(1, 1)
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self.embed = nn.Embedding(
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num_embeddings=num_embeds, embedding_dim=size, dtype=torch.bfloat16
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)
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def forward(self, batch):
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source: torch.tensor = self.source_embed(batch[:, 0])
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target: torch.tensor = self.target_embed(batch[:, 1])
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return torch.bmm(source.unsqueeze(1), target.unsqueeze(2)).squeeze()
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return self.embed(batch)
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+
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class FollowEmbedModule(pyl.LightningModule):
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def __init__(self, source_embedding, target_embedding):
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super().__init__()
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self.source_embed = source_embedding
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self.target_embed = target_embedding
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def training_step(self, batch, _):
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x, y = batch
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rotated_x = x.view(x.size(0), -1)
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source = self.source_embed(rotated_x[:, 0])
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target: torch.Tensor = self.target_embed(rotated_x[:, 1])
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dot = torch.bmm(source.unsqueeze(1), target.unsqueeze(2)).squeeze()
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loss = -1 * torch.mean(torch.log(F.sigmoid(dot * y)))
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self.log("train_loss", loss, prog_bar=False)
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return loss
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def validation_step(self, batch, _):
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x, y = batch
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rotated_x = x.view(x.size(0), -1)
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source = self.source_embed(rotated_x[:, 0])
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target: torch.Tensor = self.target_embed(rotated_x[:, 1])
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dot = torch.bmm(source.unsqueeze(1), target.unsqueeze(2)).squeeze()
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loss = -1 * torch.mean(torch.log(F.sigmoid(dot * y)))
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self.log("val_loss", loss, prog_bar=False)
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return loss
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if __name__ == "__main__":
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embeds = FollowEmbed(10, 4)
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x = torch.tensor([[1, 2], [3, 4], [5, 6]])
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print(f"Input tensor: {x.shape}")
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y = embeds(x)
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print(f"Output shape: {y.shape}")
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print(y)
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def on_validation_epoch_end(self):
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avg_loss = self.trainer.callback_metrics.get("val_loss")
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if avg_loss is not None:
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print(f"\nValidation Loss: {avg_loss:.4f}\n")
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avg_loss = self.trainer.callback_metrics.get("train_loss")
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if avg_loss is not None:
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print(f"\nTraining Loss: {avg_loss:.4f}\n")
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def configure_optimizers(self):
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optimizers = torch.optim.Adagrad(self.parameters(), lr=1e-1)
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return optimizers
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scripts/training/train.py
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scripts/training/train.py
···
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from argparse import ArgumentParser
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from datetime import datetime
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import os
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from typing import Optional
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import lightning as pyl
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from lightning.pytorch.loggers import WandbLogger
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from lightning.pytorch.callbacks import ModelCheckpoint
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import torch
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from torchdata.stateful_dataloader import StatefulDataLoader
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from scripts.training.data import FollowDataset
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from scripts.training.models import UserEmbedding, FollowEmbedModule
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PROJECT = "goodposts-followers"
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SAVE_DIR = "./data/training"
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NAME = "following-embedding"
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def main(run_id: Optional[str], model_checkpoint: Optional[str]):
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if run_id is None:
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run_id = NAME + str(int(datetime.now().timestamp()))
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+
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print("Loading validation dataset")
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val_dataset = FollowDataset(
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dataset_path="data/processed/numpy/",
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split="validation",
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negative_sample_count=0,
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)
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print("Loading training dataset")
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train_dataset = FollowDataset(
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dataset_path="data/processed/numpy/",
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split="training",
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negative_sample_count=2,
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)
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val_dataloader = StatefulDataLoader(
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val_dataset,
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pin_memory=True,
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collate_fn=val_dataset.collate_rows,
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batch_size=1024,
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num_workers=7,
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)
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+
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train_dataloader = StatefulDataLoader(
48
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train_dataset,
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pin_memory=True,
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collate_fn=train_dataset.collate_rows,
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batch_size=1024,
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num_workers=7,
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)
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+
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print("Instantiating embedding model")
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wandb_logger = WandbLogger(
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project=PROJECT,
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save_dir=SAVE_DIR,
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name=NAME,
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id=run_id,
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resume="allow",
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)
63
+
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checkpoint_callback = ModelCheckpoint(
65
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dirpath=os.path.join(SAVE_DIR, "checkpoints", run_id),
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filename="{epoch}-{step}",
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+
save_last=True,
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save_top_k=1,
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monitor="val_loss",
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)
71
+
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embedding = FollowEmbedModule(
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source_embedding=UserEmbedding(train_dataset.num_users(), 64),
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target_embedding=UserEmbedding(train_dataset.num_users(), 64),
75
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)
76
+
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torch.set_float32_matmul_precision("medium")
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trainer = pyl.Trainer(
79
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logger=wandb_logger,
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callbacks=[checkpoint_callback],
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accelerator="gpu",
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devices=1,
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precision="bf16",
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max_epochs=2,
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limit_train_batches=0.01,
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limit_val_batches=1_000,
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val_check_interval=0.05,
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)
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trainer.fit(
90
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model=embedding,
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train_dataloaders=train_dataloader,
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val_dataloaders=val_dataloader,
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ckpt_path=model_checkpoint,
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)
95
+
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+
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if __name__ == "__main__":
98
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parser = ArgumentParser()
99
+
parser.add_argument(
100
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"-r",
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"--run-id",
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dest="run_id",
103
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help="ID for training run",
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+
)
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parser.add_argument(
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"-m",
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"--model-checkpoint",
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dest="model_checkpoint",
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help="Path to checkpoint with run data",
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)
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args = parser.parse_args()
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main(run_id=args.run_id, model_checkpoint=args.model_checkpoint)
+114
uv.lock
+114
uv.lock
···
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]
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[[package]]
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+
name = "charset-normalizer"
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version = "3.4.3"
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+
source = { registry = "https://pypi.org/simple" }
306
+
sdist = { url = "https://files.pythonhosted.org/packages/83/2d/5fd176ceb9b2fc619e63405525573493ca23441330fcdaee6bef9460e924/charset_normalizer-3.4.3.tar.gz", hash = "sha256:6fce4b8500244f6fcb71465d4a4930d132ba9ab8e71a7859e6a5d59851068d14", size = 122371, upload-time = "2025-08-09T07:57:28.46Z" }
307
+
wheels = [
308
+
{ url = "https://files.pythonhosted.org/packages/d6/98/f3b8013223728a99b908c9344da3aa04ee6e3fa235f19409033eda92fb78/charset_normalizer-3.4.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fb7f67a1bfa6e40b438170ebdc8158b78dc465a5a67b6dde178a46987b244a72", size = 207695, upload-time = "2025-08-09T07:55:36.452Z" },
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366
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367
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368
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369
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370
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371
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372
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373
+
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374
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375
+
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376
+
377
+
[[package]]
303
378
name = "click"
304
379
version = "8.1.8"
305
380
source = { registry = "https://pypi.org/simple" }
···
572
647
{ name = "rich" },
573
648
{ name = "ruff" },
574
649
{ name = "torch" },
650
+
{ name = "torchdata" },
575
651
]
576
652
577
653
[package.metadata]
···
584
660
{ name = "rich", specifier = ">=14.1.0" },
585
661
{ name = "ruff", specifier = ">=0.13.2" },
586
662
{ name = "torch", specifier = ">=2.8.0" },
663
+
{ name = "torchdata", specifier = ">=0.11.0" },
587
664
]
588
665
589
666
[[package]]
···
1882
1959
]
1883
1960
1884
1961
[[package]]
1962
+
name = "requests"
1963
+
version = "2.32.5"
1964
+
source = { registry = "https://pypi.org/simple" }
1965
+
dependencies = [
1966
+
{ name = "certifi" },
1967
+
{ name = "charset-normalizer" },
1968
+
{ name = "idna" },
1969
+
{ name = "urllib3" },
1970
+
]
1971
+
sdist = { url = "https://files.pythonhosted.org/packages/c9/74/b3ff8e6c8446842c3f5c837e9c3dfcfe2018ea6ecef224c710c85ef728f4/requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf", size = 134517, upload-time = "2025-08-18T20:46:02.573Z" }
1972
+
wheels = [
1973
+
{ url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738, upload-time = "2025-08-18T20:46:00.542Z" },
1974
+
]
1975
+
1976
+
[[package]]
1885
1977
name = "rich"
1886
1978
version = "14.1.0"
1887
1979
source = { registry = "https://pypi.org/simple" }
···
2018
2110
]
2019
2111
2020
2112
[[package]]
2113
+
name = "torchdata"
2114
+
version = "0.11.0"
2115
+
source = { registry = "https://pypi.org/simple" }
2116
+
dependencies = [
2117
+
{ name = "requests" },
2118
+
{ name = "torch" },
2119
+
{ name = "urllib3" },
2120
+
]
2121
+
wheels = [
2122
+
{ url = "https://files.pythonhosted.org/packages/95/d4/af694ef718aedbe95a72760ab9ff7a6a7a44ace2d7f70c27bfeb67c5c503/torchdata-0.11.0-py3-none-any.whl", hash = "sha256:52b940fbbe0e00fb21cabddf528449d1bec5bfb0d0823b7487b15f951658ee33", size = 61968, upload-time = "2025-02-20T22:26:30.666Z" },
2123
+
]
2124
+
2125
+
[[package]]
2021
2126
name = "torchmetrics"
2022
2127
version = "1.8.2"
2023
2128
source = { registry = "https://pypi.org/simple" }
···
2091
2196
sdist = { url = "https://files.pythonhosted.org/packages/95/32/1a225d6164441be760d75c2c42e2780dc0873fe382da3e98a2e1e48361e5/tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9", size = 196380, upload-time = "2025-03-23T13:54:43.652Z" }
2092
2197
wheels = [
2093
2198
{ url = "https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8", size = 347839, upload-time = "2025-03-23T13:54:41.845Z" },
2199
+
]
2200
+
2201
+
[[package]]
2202
+
name = "urllib3"
2203
+
version = "2.5.0"
2204
+
source = { registry = "https://pypi.org/simple" }
2205
+
sdist = { url = "https://files.pythonhosted.org/packages/15/22/9ee70a2574a4f4599c47dd506532914ce044817c7752a79b6a51286319bc/urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760", size = 393185, upload-time = "2025-06-18T14:07:41.644Z" }
2206
+
wheels = [
2207
+
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2094
2208
]
2095
2209
2096
2210
[[package]]