source/train.py

from __future__ import annotations

import json
from pathlib import Path

import numpy as np
import torch
import trackio
from model import (
    VOCAB_SIZE,
    ContentAddressedMemory,
    FixedStateGRU,
    parameter_count,
)
from safetensors.torch import save_file
from torch.nn import functional as F

PROJECT_DIR = Path(__file__).resolve().parent
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "memory-tape-pocket"
DATA_DIR = PROJECT_DIR / "data"
TRAIN_SLOT_RANGE = (2, 8)
STEPS = 2_500
BATCH_SIZE = 256
SEEDS = [2281, 2287, 2293]


def sample_batch(
    batch_size: int,
    slots: int,
    generator: torch.Generator,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    keys = torch.stack(
        [torch.randperm(VOCAB_SIZE, generator=generator)[:slots] for _ in range(batch_size)]
    )
    values = torch.randint(
        VOCAB_SIZE,
        (batch_size, slots),
        generator=generator,
    )
    query_positions = torch.randint(slots, (batch_size,), generator=generator)
    rows = torch.arange(batch_size)
    query = keys[rows, query_positions]
    target = values[rows, query_positions]
    return keys, values, query, target


@torch.inference_mode()
def evaluate(
    model: torch.nn.Module,
    *,
    slots: int,
    seed: int,
    examples: int = 4_096,
) -> dict:
    generator = torch.Generator().manual_seed(seed)
    model.eval()
    correct = 0
    attention_mass = []
    for start in range(0, examples, 256):
        size = min(256, examples - start)
        keys, values, query, target = sample_batch(size, slots, generator)
        if isinstance(model, ContentAddressedMemory):
            logits, attention = model(
                keys,
                values,
                query,
                return_attention=True,
            )
            match = keys.eq(query[:, None])
            attention_mass.extend(attention[match].tolist())
        else:
            logits = model(keys, values, query)
        correct += int(logits.argmax(1).eq(target).sum())
    report = {"accuracy": correct / examples, "examples": examples}
    if attention_mass:
        report["mean_attention_on_correct_slot"] = float(np.mean(attention_mass))
    return report


def train_one(
    constructor: type[ContentAddressedMemory] | type[FixedStateGRU],
    seed: int,
) -> torch.nn.Module:
    torch.manual_seed(seed)
    generator = torch.Generator().manual_seed(seed + 1)
    model = constructor()
    optimizer = torch.optim.AdamW(model.parameters(), lr=3e-3, weight_decay=1e-5)
    for step in range(1, STEPS + 1):
        slots = int(
            torch.randint(
                TRAIN_SLOT_RANGE[0],
                TRAIN_SLOT_RANGE[1] + 1,
                (),
                generator=generator,
            )
        )
        keys, values, query, target = sample_batch(BATCH_SIZE, slots, generator)
        loss = F.cross_entropy(model(keys, values, query), target)
        optimizer.zero_grad(set_to_none=True)
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
        optimizer.step()
        if step % 250 == 0:
            trackio.log(
                {
                    "training_step": step,
                    "variant": constructor.__name__,
                    "training_loss": float(loss.detach()),
                }
            )
    return model


def write_dataset() -> None:
    generator = torch.Generator().manual_seed(23_117)
    keys, values, queries, targets = sample_batch(512, 32, generator)
    lines = []
    for index in range(len(keys)):
        lines.append(
            json.dumps(
                {
                    "keys": keys[index].tolist(),
                    "values": values[index].tolist(),
                    "query": int(queries[index]),
                    "target": int(targets[index]),
                }
            )
        )
    DATA_DIR.mkdir(parents=True, exist_ok=True)
    (DATA_DIR / "associative_recall_eval.jsonl").write_text(
        "\n".join(lines) + "\n",
        encoding="utf-8",
    )


def main() -> None:
    torch.set_num_threads(1)
    trackio.init(
        project="memory-tape-pocket",
        name="content-addressing-vs-fixed-state-v1",
        config={
            "training_slots": list(TRAIN_SLOT_RANGE),
            "steps": STEPS,
            "seeds": SEEDS,
        },
    )
    constructors = {
        "memory": ContentAddressedMemory,
        "gru": FixedStateGRU,
    }
    runs = {name: [] for name in constructors}
    saved_models = {}
    for seed in SEEDS:
        for name, constructor in constructors.items():
            model = train_one(constructor, seed)
            run = {
                "seed": seed,
                "slots_8": evaluate(model, slots=8, seed=seed + 100),
                "slots_16": evaluate(model, slots=16, seed=seed + 200),
                "slots_32": evaluate(model, slots=32, seed=seed + 300),
            }
            runs[name].append(run)
            if seed == SEEDS[0]:
                saved_models[name] = model
    results = {}
    for name, model_runs in runs.items():
        results[name] = {
            "parameters": parameter_count(saved_models[name]),
            "runs": model_runs,
            "accuracy_mean": {
                f"slots_{slots}": float(
                    np.mean(
                        [
                            run[f"slots_{slots}"]["accuracy"]
                            for run in model_runs
                        ]
                    )
                )
                for slots in [8, 16, 32]
            },
        }
        if name == "memory":
            results[name]["correct_slot_attention_mean"] = {
                f"slots_{slots}": float(
                    np.mean(
                        [
                            run[f"slots_{slots}"][
                                "mean_attention_on_correct_slot"
                            ]
                            for run in model_runs
                        ]
                    )
                )
                for slots in [8, 16, 32]
            }
    report = {
        "experiment": "Differentiable content addressing versus fixed-state recall",
        "training_slots": list(TRAIN_SLOT_RANGE),
        "results": results,
    }
    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
    save_file(
        saved_models["memory"].state_dict(),
        ARTIFACT_DIR / "content_memory.safetensors",
    )
    save_file(
        saved_models["gru"].state_dict(),
        ARTIFACT_DIR / "fixed_gru.safetensors",
    )
    (ARTIFACT_DIR / "evaluation.json").write_text(
        json.dumps(report, indent=2),
        encoding="utf-8",
    )
    write_dataset()
    trackio.log(
        {
            "memory_slots_32_mean": results["memory"]["accuracy_mean"]["slots_32"],
            "gru_slots_32_mean": results["gru"]["accuracy_mean"]["slots_32"],
        }
    )
    trackio.finish()
    print(json.dumps(report, indent=2))


if __name__ == "__main__":
    main()