hivemind/tests/test_util_modules.py

import asyncio
import concurrent.futures
import multiprocessing as mp
import random
import time

import numpy as np
import pytest
import torch

import hivemind
from hivemind.proto.dht_pb2_grpc import DHTStub
from hivemind.proto.runtime_pb2 import CompressionType
from hivemind.proto.runtime_pb2_grpc import ConnectionHandlerStub
from hivemind.utils import MSGPackSerializer
from hivemind.utils.asyncio import amap_in_executor, aiter, aenumerate, achain, anext, azip
from hivemind.utils.compression import serialize_torch_tensor, deserialize_torch_tensor
from hivemind.utils.mpfuture import InvalidStateError


@pytest.mark.forked
def test_mpfuture_result():
    future = hivemind.MPFuture()

    def _proc(future):
        with pytest.raises(RuntimeError):
            future.result()  # only creator process can await result

        future.set_result(321)

    p = mp.Process(target=_proc, args=(future,))
    p.start()
    p.join()

    assert future.result() == 321
    assert future.exception() is None
    assert future.cancel() is False
    assert future.done() and not future.running() and not future.cancelled()

    future = hivemind.MPFuture()
    with pytest.raises(concurrent.futures.TimeoutError):
        future.result(timeout=1e-3)

    future.set_result(['abacaba', 123])
    assert future.result() == ['abacaba', 123]


@pytest.mark.forked
def test_mpfuture_exception():
    future = hivemind.MPFuture()
    with pytest.raises(concurrent.futures.TimeoutError):
        future.exception(timeout=1e-3)

    def _proc(future):
        future.set_exception(NotImplementedError())

    p = mp.Process(target=_proc, args=(future,))
    p.start()
    p.join()

    assert isinstance(future.exception(), NotImplementedError)
    with pytest.raises(NotImplementedError):
        future.result()
    assert future.cancel() is False
    assert future.done() and not future.running() and not future.cancelled()


@pytest.mark.forked
def test_mpfuture_cancel():
    future = hivemind.MPFuture()
    assert not future.cancelled()
    future.cancel()
    evt = mp.Event()

    def _proc():
        with pytest.raises(concurrent.futures.CancelledError):
            future.result()
        with pytest.raises(concurrent.futures.CancelledError):
            future.exception()
        with pytest.raises(InvalidStateError):
            future.set_result(123)
        with pytest.raises(InvalidStateError):
            future.set_exception(NotImplementedError())
        assert future.cancelled() and future.done() and not future.running()
        evt.set()

    p = mp.Process(target=_proc)
    p.start()
    p.join()
    assert evt.is_set()


@pytest.mark.forked
def test_mpfuture_status():
    evt = mp.Event()
    future = hivemind.MPFuture()

    def _proc1(future):
        assert future.set_running_or_notify_cancel() is True
        evt.set()

    p = mp.Process(target=_proc1, args=(future,))
    p.start()
    p.join()
    assert evt.is_set()
    evt.clear()

    assert future.running() and not future.done() and not future.cancelled()
    with pytest.raises(InvalidStateError):
        future.set_running_or_notify_cancel()

    future = hivemind.MPFuture()
    assert future.cancel()

    def _proc2(future):
        assert not future.running() and future.done() and future.cancelled()
        assert future.set_running_or_notify_cancel() is False
        evt.set()

    p = mp.Process(target=_proc2, args=(future,))
    p.start()
    p.join()
    evt.set()

    future2 = hivemind.MPFuture()
    future2.cancel()
    assert future2.set_running_or_notify_cancel() is False


@pytest.mark.asyncio
async def test_await_mpfuture():
    # await result from the same process, but a different coroutine
    f1, f2 = hivemind.MPFuture(), hivemind.MPFuture()

    async def wait_and_assign_async():
        assert f2.set_running_or_notify_cancel() is True
        await asyncio.sleep(0.1)
        f1.set_result((123, 'ololo'))
        f2.set_result((456, 'pyshpysh'))

    asyncio.create_task(wait_and_assign_async())

    assert (await asyncio.gather(f1, f2)) == [(123, 'ololo'), (456, 'pyshpysh')]

    # await result from separate processes
    f1, f2 = hivemind.MPFuture(), hivemind.MPFuture()

    def wait_and_assign(future, value):
        time.sleep(0.1 * random.random())
        future.set_result(value)

    p1 = mp.Process(target=wait_and_assign, args=(f1, 'abc'))
    p2 = mp.Process(target=wait_and_assign, args=(f2, 'def'))
    for p in p1, p2:
        p.start()

    assert (await asyncio.gather(f1, f2)) == ['abc', 'def']
    for p in p1, p2:
        p.join()

    # await cancel
    f1, f2 = hivemind.MPFuture(), hivemind.MPFuture()

    def wait_and_cancel():
        time.sleep(0.01)
        f2.set_result(123456)
        time.sleep(0.1)
        f1.cancel()

    p = mp.Process(target=wait_and_cancel)
    p.start()

    with pytest.raises(asyncio.CancelledError):
        # note: it is intended that MPFuture raises Cancel
        await asyncio.gather(f1, f2)

    p.join()

    # await exception
    f1, f2 = hivemind.MPFuture(), hivemind.MPFuture()

    def wait_and_raise():
        time.sleep(0.01)
        f2.set_result(123456)
        time.sleep(0.1)
        f1.set_exception(ValueError('we messed up'))

    p = mp.Process(target=wait_and_raise)
    p.start()

    with pytest.raises(ValueError):
        # note: it is intended that MPFuture raises Cancel
        await asyncio.gather(f1, f2)

    p.join()


@pytest.mark.forked
def test_mpfuture_bidirectional():
    evt = mp.Event()
    future_from_main = hivemind.MPFuture()

    def _future_creator():
        future_from_fork = hivemind.MPFuture()
        future_from_main.set_result(('abc', future_from_fork))

        if future_from_fork.result() == ['we', 'need', 'to', 'go', 'deeper']:
            evt.set()

    p = mp.Process(target=_future_creator)
    p.start()

    out = future_from_main.result()
    assert isinstance(out[1], hivemind.MPFuture)
    out[1].set_result(['we', 'need', 'to', 'go', 'deeper'])

    p.join()
    assert evt.is_set()


@pytest.mark.forked
def test_mpfuture_done_callback():
    receiver, sender = mp.Pipe(duplex=False)
    events = [mp.Event() for _ in range(5)]

    def _future_creator():
        future1, future2, future3 = hivemind.MPFuture(), hivemind.MPFuture(), hivemind.MPFuture()

        def _check_result_and_set(future):
            assert future.done()
            assert future.result() == 123
            events[0].set()

        future1.add_done_callback(_check_result_and_set)
        future1.add_done_callback(lambda future: events[1].set())
        future2.add_done_callback(lambda future: events[2].set())
        future3.add_done_callback(lambda future: events[3].set())

        sender.send((future1, future2))
        future2.cancel()  # trigger future2 callback from the same process

        events[0].wait()
        future1.add_done_callback(lambda future: events[4].set())  # schedule callback after future1 is already finished

    p = mp.Process(target=_future_creator)
    p.start()

    future1, future2 = receiver.recv()
    future1.set_result(123)

    with pytest.raises(RuntimeError):
        future1.add_done_callback(lambda future: (1, 2, 3))

    p.join()
    events[0].wait(1)
    events[1].wait(1)
    assert future1.done() and not future1.cancelled()
    assert future2.done() and future2.cancelled()
    assert events[0].is_set() and events[1].is_set() and events[2].is_set() and events[4].is_set()
    assert not events[3].is_set()


@pytest.mark.forked
def test_many_futures():
    evt = mp.Event()
    receiver, sender = mp.Pipe()
    main_futures = [hivemind.MPFuture() for _ in range(1000)]
    assert len(hivemind.MPFuture._active_futures) == 1000

    def _run_peer():
        fork_futures = [hivemind.MPFuture() for _ in range(500)]
        assert len(hivemind.MPFuture._active_futures) == 500

        for i, future in enumerate(random.sample(main_futures, 300)):
            if random.random() < 0.5:
                future.set_result(i)
            else:
                future.set_exception(ValueError(f"{i}"))

        sender.send(fork_futures[:-100])
        for future in fork_futures[-100:]:
            future.cancel()

        evt.wait()

        assert len(hivemind.MPFuture._active_futures) == 200
        for future in fork_futures:
            future.cancel()
        assert len(hivemind.MPFuture._active_futures) == 0

    p = mp.Process(target=_run_peer)
    p.start()

    some_fork_futures = receiver.recv()
    assert len(hivemind.MPFuture._active_futures) == 700

    for future in some_fork_futures:
        future.set_running_or_notify_cancel()
    for future in random.sample(some_fork_futures, 200):
        future.set_result(321)

    time.sleep(0.5)
    evt.set()
    for future in main_futures:
        future.cancel()
    assert len(hivemind.MPFuture._active_futures) == 0
    p.join()


def test_tensor_compression(size=(128, 128, 64), alpha=5e-08, beta=0.0008):
    torch.manual_seed(0)
    X = torch.randn(*size)
    assert torch.allclose(deserialize_torch_tensor(serialize_torch_tensor(X, CompressionType.NONE)), X)
    error = deserialize_torch_tensor(serialize_torch_tensor(X, CompressionType.MEANSTD_16BIT)) - X
    assert error.square().mean() < alpha
    error = deserialize_torch_tensor(serialize_torch_tensor(X, CompressionType.FLOAT16)) - X
    assert error.square().mean() < alpha
    error = deserialize_torch_tensor(serialize_torch_tensor(X, CompressionType.QUANTILE_8BIT)) - X
    assert error.square().mean() < beta
    error = deserialize_torch_tensor(serialize_torch_tensor(X, CompressionType.UNIFORM_8BIT)) - X
    assert error.square().mean() < beta

    zeros = torch.zeros(5, 5)
    for compression_type in CompressionType.values():
        assert deserialize_torch_tensor(serialize_torch_tensor(zeros, compression_type)).isfinite().all()


@pytest.mark.forked
@pytest.mark.asyncio
async def test_channel_cache():
    hivemind.ChannelCache.MAXIMUM_CHANNELS = 3
    hivemind.ChannelCache.EVICTION_PERIOD_SECONDS = 0.1

    c1 = hivemind.ChannelCache.get_stub('localhost:1337', DHTStub, aio=False)
    c2 = hivemind.ChannelCache.get_stub('localhost:1337', DHTStub, aio=True)
    c3 = hivemind.ChannelCache.get_stub('localhost:1338', DHTStub, aio=False)
    c3_again = hivemind.ChannelCache.get_stub('localhost:1338', DHTStub, aio=False)
    c1_again = hivemind.ChannelCache.get_stub('localhost:1337', DHTStub, aio=False)
    c4 = hivemind.ChannelCache.get_stub('localhost:1339', DHTStub, aio=True)
    c2_anew = hivemind.ChannelCache.get_stub('localhost:1337', DHTStub, aio=True)
    c1_yetagain = hivemind.ChannelCache.get_stub('localhost:1337', DHTStub, aio=False)

    await asyncio.sleep(0.2)
    c1_anew = hivemind.ChannelCache.get_stub(target='localhost:1337', aio=False, stub_type=DHTStub)
    c1_anew_again = hivemind.ChannelCache.get_stub(target='localhost:1337', aio=False, stub_type=DHTStub)
    c1_otherstub = hivemind.ChannelCache.get_stub(target='localhost:1337', aio=False, stub_type=ConnectionHandlerStub)
    await asyncio.sleep(0.05)
    c1_otherstub_again = hivemind.ChannelCache.get_stub(target='localhost:1337', aio=False,
                                                        stub_type=ConnectionHandlerStub)
    all_channels = [c1, c2, c3, c4, c3_again, c1_again, c2_anew, c1_yetagain, c1_anew, c1_anew_again, c1_otherstub]

    assert all(isinstance(c, DHTStub) for c in all_channels[:-1])
    assert isinstance(all_channels[-1], ConnectionHandlerStub)
    assert 'aio' in repr(c2.rpc_find)
    assert 'aio' not in repr(c1.rpc_find)

    duplicates = {(c1, c1_again), (c1, c1_yetagain), (c1_again, c1_yetagain), (c3, c3_again),
                  (c1_anew, c1_anew_again), (c1_otherstub, c1_otherstub_again)}
    for i in range(len(all_channels)):
        for j in range(i + 1, len(all_channels)):
            ci, cj = all_channels[i], all_channels[j]
            assert (ci is cj) == ((ci, cj) in duplicates), (i, j)


def test_serialize_tensor():
    tensor = torch.randn(512, 12288)

    serialized_tensor = serialize_torch_tensor(tensor, CompressionType.NONE)
    for chunk_size in [1024, 64 * 1024, 64 * 1024 + 1, 10 ** 9]:
        chunks = list(hivemind.split_for_streaming(serialized_tensor, chunk_size))
        assert len(chunks) == (len(serialized_tensor.buffer) - 1) // chunk_size + 1
        restored = hivemind.combine_from_streaming(chunks)
        assert torch.allclose(deserialize_torch_tensor(restored), tensor)

    chunk_size = 30 * 1024
    serialized_tensor = serialize_torch_tensor(tensor, CompressionType.FLOAT16)
    chunks = list(hivemind.split_for_streaming(serialized_tensor, chunk_size))
    assert len(chunks) == (len(serialized_tensor.buffer) - 1) // chunk_size + 1
    restored = hivemind.combine_from_streaming(chunks)
    assert torch.allclose(deserialize_torch_tensor(restored), tensor, rtol=0, atol=1e-2)

    tensor = torch.randint(0, 100, (512, 1, 1))
    serialized_tensor = serialize_torch_tensor(tensor, CompressionType.NONE)
    chunks = list(hivemind.split_for_streaming(serialized_tensor, chunk_size))
    assert len(chunks) == (len(serialized_tensor.buffer) - 1) // chunk_size + 1
    restored = hivemind.combine_from_streaming(chunks)
    assert torch.allclose(deserialize_torch_tensor(restored), tensor)

    scalar = torch.tensor(1.)
    serialized_scalar = serialize_torch_tensor(scalar, CompressionType.NONE)
    assert torch.allclose(deserialize_torch_tensor(serialized_scalar), scalar)

    serialized_scalar = serialize_torch_tensor(scalar, CompressionType.FLOAT16)
    assert torch.allclose(deserialize_torch_tensor(serialized_scalar), scalar)


def test_serialize_tuple():
    test_pairs = (
        ((1, 2, 3), [1, 2, 3]),
        (('1', False, 0), ['1', False, 0]),
        (('1', False, 0), ('1', 0, 0)),
        (('1', b'qq', (2, 5, '0')), ['1', b'qq', (2, 5, '0')]),
    )

    for first, second in test_pairs:
        assert MSGPackSerializer.loads(MSGPackSerializer.dumps(first)) == first
        assert MSGPackSerializer.loads(MSGPackSerializer.dumps(second)) == second
        assert MSGPackSerializer.dumps(first) != MSGPackSerializer.dumps(second)


def test_split_parts():
    tensor = torch.randn(910, 512)
    serialized_tensor_part = serialize_torch_tensor(tensor, allow_inplace=False)
    chunks1 = list(hivemind.utils.split_for_streaming(serialized_tensor_part, 16384))
    assert len(chunks1) == int(np.ceil(tensor.numel() * tensor.element_size() / 16384))

    chunks2 = list(hivemind.utils.split_for_streaming(serialized_tensor_part, 10_000))
    assert len(chunks2) == int(np.ceil(tensor.numel() * tensor.element_size() / 10_000))

    chunks3 = list(hivemind.utils.split_for_streaming(serialized_tensor_part, 10 ** 9))
    assert len(chunks3) == 1

    compressed_tensor_part = serialize_torch_tensor(tensor, CompressionType.FLOAT16, allow_inplace=False)
    chunks4 = list(hivemind.utils.split_for_streaming(compressed_tensor_part, 16384))
    assert len(chunks4) == int(np.ceil(tensor.numel() * 2 / 16384))

    combined1 = hivemind.utils.combine_from_streaming(chunks1)
    combined2 = hivemind.utils.combine_from_streaming(iter(chunks2))
    combined3 = hivemind.utils.combine_from_streaming(chunks3)
    combined4 = hivemind.utils.combine_from_streaming(chunks4)
    for combined in combined1, combined2, combined3:
        assert torch.allclose(tensor, deserialize_torch_tensor(combined), rtol=1e-5, atol=1e-8)

    assert torch.allclose(tensor, deserialize_torch_tensor(combined4), rtol=1e-3, atol=1e-3)

    combined_incomplete = hivemind.utils.combine_from_streaming(chunks4[:5])
    combined_incomplete2 = hivemind.utils.combine_from_streaming(chunks4[:1])
    combined_incomplete3 = hivemind.utils.combine_from_streaming(chunks4[:-1])
    for combined in combined_incomplete, combined_incomplete2, combined_incomplete3:
        with pytest.raises(RuntimeError):
            deserialize_torch_tensor(combined)
            # note: we rely on this being RuntimeError in hivemind.averaging.allreduce.AllreduceRunner


def test_generic_data_classes():
    from hivemind.utils import ValueWithExpiration, HeapEntry, DHTExpiration

    value_with_exp = ValueWithExpiration(value="string_value", expiration_time=DHTExpiration(10))
    assert value_with_exp.value == "string_value" and value_with_exp.expiration_time == DHTExpiration(10)

    heap_entry = HeapEntry(expiration_time=DHTExpiration(10), key="string_value")
    assert heap_entry.key == "string_value" and heap_entry.expiration_time == DHTExpiration(10)

    sorted_expirations = sorted([DHTExpiration(value) for value in range(1, 1000)])
    sorted_heap_entries = sorted([HeapEntry(DHTExpiration(value), key="any") for value in range(1, 1000)[::-1]])
    assert all([entry.expiration_time == value for entry, value in zip(sorted_heap_entries, sorted_expirations)])


@pytest.mark.asyncio
async def test_asyncio_utils():
    res = [i async for i, item in aenumerate(aiter('a', 'b', 'c'))]
    assert res == list(range(len(res)))

    num_steps = 0
    async for elem in amap_in_executor(lambda x: x ** 2, aiter(*range(100)), max_prefetch=5):
        assert elem == num_steps ** 2
        num_steps += 1
    assert num_steps == 100

    ours = [elem async for elem in amap_in_executor(max, aiter(*range(7)), aiter(*range(-50, 50, 10)), max_prefetch=1)]
    ref = list(map(max, range(7), range(-50, 50, 10)))
    assert ours == ref

    ours = [row async for row in azip(aiter('a', 'b', 'c'), aiter(1, 2, 3))]
    ref = list(zip(['a', 'b', 'c'], [1, 2, 3]))
    assert ours == ref

    async def _aiterate():
        yield 'foo'
        yield 'bar'
        yield 'baz'

    iterator = _aiterate()
    assert (await anext(iterator)) == 'foo'
    tail = [item async for item in iterator]
    assert tail == ['bar', 'baz']
    with pytest.raises(StopAsyncIteration):
        await anext(iterator)

    assert [item async for item in achain(_aiterate(), aiter(*range(5)))] == ['foo', 'bar', 'baz'] + list(range(5))