hivemind/tests/test_training.py

import time
from functools import partial

import pytest
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.datasets import load_digits

from hivemind import DHT
from hivemind.moe.client import RemoteMixtureOfExperts, RemoteSwitchMixtureOfExperts
from hivemind.moe.client.expert import create_remote_experts
from hivemind.moe.expert_uid import ExpertInfo
from hivemind.moe.server import background_server
from hivemind.optim import DecentralizedAdam, DecentralizedSGD


@pytest.mark.forked
def test_training(max_steps: int = 100, threshold: float = 0.9):
    dataset = load_digits(n_class=2)
    X_train, y_train = torch.tensor(dataset["data"], dtype=torch.float), torch.tensor(dataset["target"])
    SGD = partial(torch.optim.SGD, lr=0.05)

    with background_server(
        num_experts=2, device="cpu", optim_cls=SGD, hidden_dim=64, num_handlers=1
    ) as server_peer_info:
        dht = DHT(initial_peers=server_peer_info.addrs, start=True)
        expert1, expert2 = create_remote_experts(
            [
                ExpertInfo(uid="expert.0", peer_id=server_peer_info.peer_id),
                ExpertInfo(uid="expert.1", peer_id=server_peer_info.peer_id),
            ],
            dht=dht,
        )
        model = nn.Sequential(expert2, nn.ReLU(), expert1, nn.Linear(64, 2))

        opt = SGD(model.parameters(), lr=0.05)

        for step in range(max_steps):
            outputs = model(X_train)
            loss = F.cross_entropy(outputs, y_train)
            loss.backward()
            opt.step()
            opt.zero_grad()

            accuracy = (outputs.argmax(dim=1) == y_train).float().mean().item()
            if accuracy >= threshold:
                break

        assert accuracy >= threshold, f"too small accuracy: {accuracy}"


@pytest.mark.forked
def test_moe_training(max_steps: int = 100, threshold: float = 0.9, num_experts=2):
    dataset = load_digits(n_class=2)
    X_train, y_train = torch.tensor(dataset["data"], dtype=torch.float), torch.tensor(dataset["target"])
    subsample_ix = torch.randint(0, len(X_train), (32,))
    X_train, y_train = X_train[subsample_ix], y_train[subsample_ix]
    SGD = partial(torch.optim.SGD, lr=0.05)

    all_expert_uids = [f"expert.{i}" for i in range(num_experts)]
    with background_server(
        expert_uids=all_expert_uids, device="cpu", optim_cls=SGD, hidden_dim=64, num_handlers=1
    ) as server_peer_info:
        dht = DHT(start=True, initial_peers=server_peer_info.addrs)

        moe = RemoteMixtureOfExperts(in_features=64, grid_size=(num_experts,), dht=dht, uid_prefix="expert.", k_best=2)
        model = nn.Sequential(moe, nn.Linear(64, 2))

        opt = SGD(model.parameters(), lr=0.05)

        for step in range(max_steps):
            outputs = model(X_train)
            loss = F.cross_entropy(outputs, y_train)
            loss.backward()
            opt.step()
            opt.zero_grad()

            accuracy = (outputs.argmax(dim=1) == y_train).float().mean().item()
            if accuracy >= threshold:
                break

        assert accuracy >= threshold, f"too small accuracy: {accuracy}"


class SwitchNetwork(nn.Module):
    def __init__(self, dht, in_features, num_classes, num_experts):
        super().__init__()
        self.moe = RemoteSwitchMixtureOfExperts(
            in_features=in_features,
            grid_size=(num_experts,),
            dht=dht,
            jitter_eps=0,
            uid_prefix="expert.",
            k_best=1,
            k_min=1,
        )
        self.linear = nn.Linear(in_features, num_classes)

    def forward(self, x):
        moe_output, balancing_loss = self.moe(x)
        return self.linear(moe_output), balancing_loss


@pytest.mark.forked
def test_switch_training(max_steps: int = 10, threshold: float = 0.9, num_experts=5):
    dataset = load_digits(n_class=2)
    X_train, y_train = torch.tensor(dataset["data"], dtype=torch.float), torch.tensor(dataset["target"])
    subsample_ix = torch.randint(0, len(X_train), (32,))
    X_train, y_train = X_train[subsample_ix], y_train[subsample_ix]

    SGD = partial(torch.optim.SGD, lr=0.05)

    all_expert_uids = [f"expert.{i}" for i in range(num_experts)]
    with background_server(
        expert_uids=all_expert_uids, device="cpu", optim_cls=SGD, hidden_dim=64, num_handlers=1
    ) as server_peer_info:
        dht = DHT(start=True, initial_peers=server_peer_info.addrs)

        model = SwitchNetwork(dht, 64, 2, num_experts)
        opt = SGD(model.parameters(), lr=0.05)

        for step in range(max_steps):
            outputs, balancing_loss = model(X_train)
            loss = F.cross_entropy(outputs, y_train) + 0.01 * balancing_loss
            loss.backward()
            opt.step()
            opt.zero_grad()

            accuracy = (outputs.argmax(dim=1) == y_train).float().mean().item()
            if accuracy >= threshold:
                break

        assert model.moe.grid_utilization.min().item() > (1 / num_experts) / 2
        assert accuracy >= threshold, f"too small accuracy: {accuracy}"


@pytest.mark.forked
def test_decentralized_optimizer_step():
    dht_root = DHT(start=True)
    initial_peers = dht_root.get_visible_maddrs()

    param1 = torch.nn.Parameter(torch.zeros(32, 32), requires_grad=True)
    opt1 = DecentralizedSGD(
        [param1],
        lr=0.1,
        dht=DHT(initial_peers=initial_peers, start=True),
        prefix="foo",
        target_group_size=2,
        verbose=True,
    )

    param2 = torch.nn.Parameter(torch.ones(32, 32), requires_grad=True)
    opt2 = DecentralizedSGD(
        [param2],
        lr=0.05,
        dht=DHT(initial_peers=initial_peers, start=True),
        prefix="foo",
        target_group_size=2,
        verbose=True,
    )

    assert not torch.allclose(param1, param2)

    (param1.sum() + 300 * param2.sum()).backward()

    for i in range(5):
        time.sleep(0.1)
        opt1.step()
        opt2.step()
        opt1.zero_grad()
        opt2.zero_grad()

    assert torch.allclose(param1, param2)
    reference = 0.5 * (0.0 - 0.1 * 1.0) + 0.5 * (1.0 - 0.05 * 300)
    assert torch.allclose(param1, torch.full_like(param1, reference))


@pytest.mark.skip(reason="Skipped until a more stable averager implementation is ready (TODO @justheuristic)")
@pytest.mark.forked
def test_decentralized_optimizer_averaging():
    dht_root = DHT(start=True)
    initial_peers = dht_root.get_visible_maddrs()

    param1 = torch.nn.Parameter(torch.zeros(32, 32), requires_grad=True)
    opt1 = DecentralizedAdam(
        [param1],
        lr=0.1,
        averaging_steps_period=1,
        dht=DHT(initial_peers=initial_peers, start=True),
        prefix="foo",
        target_group_size=2,
        verbose=True,
    )

    param2 = torch.nn.Parameter(torch.ones(32, 32), requires_grad=True)
    opt2 = DecentralizedAdam(
        [param2],
        lr=0.05,
        averaging_steps_period=1,
        dht=DHT(initial_peers=initial_peers, start=True),
        prefix="foo",
        target_group_size=2,
        verbose=True,
    )

    assert not torch.allclose(param1, param2, atol=1e-3, rtol=0)
    (param1.sum() + param2.sum()).backward()

    for _ in range(100):
        time.sleep(0.1)
        opt1.step()
        opt2.step()
        opt1.zero_grad()
        opt2.zero_grad()

    assert torch.allclose(param1, param2, atol=1e-3, rtol=0)
    assert torch.allclose(opt1.state[param1]["exp_avg_sq"], opt2.state[param2]["exp_avg_sq"], atol=1e-3, rtol=0)