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import json
import random
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import trackio
from model import (
DenseControl,
PocketMoE,
active_parameter_count,
parameter_count,
)
from safetensors.torch import save_file
from sklearn.metrics import accuracy_score, f1_score
from torch.nn import functional as F
from torch.utils.data import DataLoader, TensorDataset
PROJECT_DIR = Path(__file__).resolve().parent
ROOT_DIR = PROJECT_DIR.parents[1]
DATA_DIR = ROOT_DIR / "projects" / "tiny-vision-foundry" / "data"
ARTIFACT_DIR = PROJECT_DIR / "artifacts"
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def load_split(name: str) -> tuple[torch.Tensor, torch.Tensor]:
frame = pd.read_parquet(DATA_DIR / f"{name}.parquet")
pixels = np.stack(frame["image"].to_numpy()).astype(np.float32) / 16.0
labels = frame["label"].to_numpy(dtype=np.int64, copy=True)
return torch.from_numpy(pixels), torch.from_numpy(labels)
@torch.inference_mode()
def evaluate_dense(
model: DenseControl,
pixels: torch.Tensor,
labels: torch.Tensor,
) -> dict:
model.eval()
predictions = model(pixels).argmax(dim=1).numpy()
return {
"accuracy": float(accuracy_score(labels.numpy(), predictions)),
"macro_f1": float(f1_score(labels.numpy(), predictions, average="macro")),
}
@torch.inference_mode()
def evaluate_moe(
model: PocketMoE,
pixels: torch.Tensor,
labels: torch.Tensor,
) -> dict:
model.eval()
logits, router_probabilities, sparse_weights = model(pixels)
predictions = logits.argmax(dim=1).numpy()
utilization = sparse_weights.mean(dim=0).numpy()
dominant_by_class = {}
for label in range(10):
selected = labels == label
class_utilization = sparse_weights[selected].mean(dim=0)
dominant_by_class[str(label)] = {
"expert": int(class_utilization.argmax()),
"routing_share": float(class_utilization.max()),
}
entropy = -(
router_probabilities * torch.log(torch.clamp(router_probabilities, min=1e-9))
).sum(dim=1)
return {
"accuracy": float(accuracy_score(labels.numpy(), predictions)),
"macro_f1": float(f1_score(labels.numpy(), predictions, average="macro")),
"expert_utilization": utilization.tolist(),
"utilization_coefficient_of_variation": float(
utilization.std() / utilization.mean()
),
"mean_router_entropy": float(entropy.mean()),
"maximum_router_entropy": float(np.log(model.expert_count)),
"dominant_expert_by_digit": dominant_by_class,
}
def train_dense(
train_pixels: torch.Tensor,
train_labels: torch.Tensor,
validation_pixels: torch.Tensor,
validation_labels: torch.Tensor,
) -> tuple[DenseControl, dict]:
seed_everything(2042)
model = DenseControl()
loader = DataLoader(
TensorDataset(train_pixels, train_labels),
batch_size=64,
shuffle=True,
generator=torch.Generator().manual_seed(2042),
)
optimizer = torch.optim.AdamW(model.parameters(), lr=0.0025, weight_decay=0.002)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=90)
best_accuracy = -1.0
best_epoch = 0
best_state = None
for epoch in range(1, 91):
model.train()
for pixels, labels in loader:
loss = F.cross_entropy(model(pixels), labels)
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
scheduler.step()
validation = evaluate_dense(model, validation_pixels, validation_labels)
if validation["accuracy"] > best_accuracy:
best_accuracy = validation["accuracy"]
best_epoch = epoch
best_state = {
key: value.detach().cpu().clone()
for key, value in model.state_dict().items()
}
assert best_state is not None
model.load_state_dict(best_state)
return model, {
"best_epoch": best_epoch,
"best_validation_accuracy": best_accuracy,
}
def train_moe(
train_pixels: torch.Tensor,
train_labels: torch.Tensor,
validation_pixels: torch.Tensor,
validation_labels: torch.Tensor,
) -> tuple[PocketMoE, dict]:
seed_everything(2042)
model = PocketMoE()
loader = DataLoader(
TensorDataset(train_pixels, train_labels),
batch_size=64,
shuffle=True,
generator=torch.Generator().manual_seed(2042),
)
optimizer = torch.optim.AdamW(model.parameters(), lr=0.0025, weight_decay=0.002)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=90)
best_accuracy = -1.0
best_epoch = 0
best_state = None
for epoch in range(1, 91):
model.train()
losses = []
for pixels, labels in loader:
logits, router_probabilities, _ = model(pixels)
classification = F.cross_entropy(logits, labels)
importance = router_probabilities.mean(dim=0)
balance = ((importance * model.expert_count - 1) ** 2).mean()
loss = classification + 0.025 * balance
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
losses.append(loss.item())
scheduler.step()
validation = evaluate_moe(model, validation_pixels, validation_labels)
if validation["accuracy"] > best_accuracy:
best_accuracy = validation["accuracy"]
best_epoch = epoch
best_state = {
key: value.detach().cpu().clone()
for key, value in model.state_dict().items()
}
if epoch == 1 or epoch % 10 == 0:
trackio.log(
{
"epoch": epoch,
"moe_train_loss": float(np.mean(losses)),
"moe_validation_accuracy": validation["accuracy"],
"moe_utilization_cv": validation[
"utilization_coefficient_of_variation"
],
"learning_rate": scheduler.get_last_lr()[0],
}
)
assert best_state is not None
model.load_state_dict(best_state)
return model, {
"best_epoch": best_epoch,
"best_validation_accuracy": best_accuracy,
}
def main() -> None:
train_pixels, train_labels = load_split("train")
validation_pixels, validation_labels = load_split("validation")
test_pixels, test_labels = load_split("test")
trackio.init(
project="pocket-moe",
name="top2-versus-dense-v1",
config={
"experts": 4,
"active_experts": 2,
"moe_parameters": parameter_count(PocketMoE()),
"dense_parameters": parameter_count(DenseControl()),
},
)
dense, dense_training = train_dense(
train_pixels,
train_labels,
validation_pixels,
validation_labels,
)
moe, moe_training = train_moe(
train_pixels,
train_labels,
validation_pixels,
validation_labels,
)
dense_test = evaluate_dense(dense, test_pixels, test_labels)
moe_test = evaluate_moe(moe, test_pixels, test_labels)
results = {
"model": "Pocket MoE",
"experts": moe.expert_count,
"active_experts_per_example": moe.top_k,
"stored_parameters": parameter_count(moe),
"active_parameters_per_example": active_parameter_count(moe),
"dense_control_parameters": parameter_count(dense),
"moe_training": moe_training,
"dense_training": dense_training,
"moe_test": moe_test,
"dense_control_test": dense_test,
"accuracy_delta": moe_test["accuracy"] - dense_test["accuracy"],
}
trackio.log(
{
"moe_test_accuracy": moe_test["accuracy"],
"dense_test_accuracy": dense_test["accuracy"],
"moe_test_utilization_cv": moe_test["utilization_coefficient_of_variation"],
}
)
trackio.finish()
moe_dir = ARTIFACT_DIR / "pocket-moe"
dense_dir = ARTIFACT_DIR / "dense-control"
moe_dir.mkdir(parents=True, exist_ok=True)
dense_dir.mkdir(parents=True, exist_ok=True)
save_file(moe.state_dict(), moe_dir / "model.safetensors")
save_file(dense.state_dict(), dense_dir / "model.safetensors")
(moe_dir / "evaluation.json").write_text(
json.dumps(results, indent=2),
encoding="utf-8",
)
print(json.dumps(results, indent=2))
if __name__ == "__main__":
main()
|