Merge branch 'main' of https://github.com/bigscience-workshop/petals into danO
Danny Boy
9 months ago
commit
9027270240
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import asyncio
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import multiprocessing as mp
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import random
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import time
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from typing import Optional
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import pytest
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import pytest_asyncio # make sure the module exists; otherwise the test will be skipped
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import torch
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from hivemind import TensorDescriptor
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from petals.server.memory_cache import AllocationFailed, MemoryCache
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from petals.utils.misc import get_size_in_bytes
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def _make_tensor_descriptor(num_bytes: int, dtype: Optional[torch.dtype] = None):
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if dtype is None:
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dtype = random.choice((torch.int64, torch.int8, torch.uint8, torch.float32, torch.bfloat16, torch.bool))
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elem_size_bytes = get_size_in_bytes(dtype)
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descr = TensorDescriptor.from_tensor(torch.empty((num_bytes // elem_size_bytes,), dtype=dtype))
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return descr
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@pytest.mark.asyncio
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async def test_cache_timeout():
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cache = MemoryCache(max_size_bytes=1024, max_alloc_timeout=0.5)
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cache.runtime_pid += 1 # pretend we're another process
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async with cache.allocate_cache(_make_tensor_descriptor(768), timeout=0):
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pass
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async with cache.allocate_cache(_make_tensor_descriptor(100), timeout=999):
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async with cache.allocate_cache(_make_tensor_descriptor(512), timeout=0):
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async with cache.allocate_cache(_make_tensor_descriptor(128), _make_tensor_descriptor(32), timeout=1):
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t_start = time.perf_counter()
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with pytest.raises(AllocationFailed):
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async with cache.allocate_cache(_make_tensor_descriptor(768), timeout=0.1):
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pass
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assert 0.1 < time.perf_counter() - t_start < 0.2, "wait time exceeds alloc timeout"
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async with cache.allocate_cache(_make_tensor_descriptor(128), timeout=float("inf")):
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pass
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t_start = time.perf_counter()
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with pytest.raises(AllocationFailed):
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async with cache.allocate_cache(_make_tensor_descriptor(384), timeout=1.0): # exceeds max timeout
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pass
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assert 0.5 < time.perf_counter() - t_start < 0.6, "wait time exceeds max alloc timeout"
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# test memory allocation when another task frees the memory
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async def _klog_the_cache():
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async with cache.allocate_cache(_make_tensor_descriptor(512), timeout=0.2):
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pass
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large_alloc_task = asyncio.create_task(_klog_the_cache())
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t_start = time.perf_counter()
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await asyncio.sleep(0.05) # wait for large alloc to enqueue
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async with cache.allocate_cache(_make_tensor_descriptor(128), timeout=float("inf")): # exceeds max timeout
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pass # this memory should allocate once the background task clears the queue
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assert 0.2 < time.perf_counter() - t_start < 0.3, "memory should be allocated after background task clears"
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with pytest.raises(AllocationFailed):
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await large_alloc_task
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# test that zero-timeout allocation fails instantaneously even if someone else is awaiting alloc
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large_alloc_task = asyncio.create_task(_klog_the_cache())
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t_start = time.perf_counter()
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await asyncio.sleep(0.05) # wait for large alloc to enqueue
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with pytest.raises(AllocationFailed):
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async with cache.allocate_cache(_make_tensor_descriptor(512), timeout=0):
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pass # this memory should allocate once the background task clears the queue
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assert time.perf_counter() - t_start < 0.1, "zero-timeout task should fail (or succeed) instantaneously"
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with pytest.raises(AllocationFailed):
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await large_alloc_task
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@pytest.mark.asyncio
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async def test_unlimited_timeout():
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cache = MemoryCache(max_size_bytes=1024)
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cache.runtime_pid += 1 # pretend we're another process
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t_start = time.perf_counter()
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async def _klog_the_cache():
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async with cache.allocate_cache(_make_tensor_descriptor(512), timeout=0.2):
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await asyncio.sleep(0.5)
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alloc_task = asyncio.create_task(_klog_the_cache())
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await asyncio.sleep(0.1)
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async with cache.allocate_cache(_make_tensor_descriptor(768), timeout=float("inf")):
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await alloc_task
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assert 0.5 < time.perf_counter() - t_start < 0.6, "memory should be allocated after background task clears"
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@pytest.mark.asyncio
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async def test_cache_usage():
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cache = MemoryCache(max_size_bytes=2048)
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alloc_event, dealloc_a_event, dealloc_bcd_event, dealloc_e_event, dealloc_f_event = (mp.Event() for _ in range(5))
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pipe_receiver, pipe_sender = mp.Pipe(duplex=False)
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with pytest.raises(AssertionError):
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async with cache.allocate_cache(_make_tensor_descriptor(123), timeout=1):
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pass # fails because cache must be allocated from another process
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descr_a = TensorDescriptor.from_tensor(torch.empty(768, dtype=torch.uint8)) # 768 bytes
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descr_b = TensorDescriptor.from_tensor(torch.empty((), dtype=torch.float64)) # 8 bytes
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descr_c = TensorDescriptor.from_tensor(torch.empty((33,), dtype=torch.bool)) # 33 bytes
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descr_d = TensorDescriptor.from_tensor(torch.empty((0,), dtype=torch.int64)) # 0 bytes
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descr_e = TensorDescriptor.from_tensor(torch.empty((96, 8), dtype=torch.bfloat16)) # 1536 bytes
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descr_f = TensorDescriptor.from_tensor(torch.empty((1792,), dtype=torch.uint8)) # 1792 bytes
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async def _allocate_and_wait(dealloc_event, *descrs, timeout=None):
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loop = asyncio.get_event_loop()
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async with cache.allocate_cache(*descrs, timeout=timeout) as handles:
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pipe_sender.send(handles)
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await loop.run_in_executor(None, dealloc_event.wait)
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async def _allocate_af():
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alloc_event.wait()
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allocate_a_task = asyncio.create_task(_allocate_and_wait(dealloc_a_event, descr_a))
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await allocate_a_task
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allocate_f_task = asyncio.create_task(_allocate_and_wait(dealloc_f_event, descr_f)) # klogs the cache
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await allocate_f_task
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alloc_process1 = mp.context.ForkProcess(target=lambda: asyncio.run(_allocate_af()), daemon=True)
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alloc_process1.start()
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async def _allocate_bcde():
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alloc_event.wait()
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await asyncio.sleep(0.1) # ensure that the other tensor is always allocated (and sent through pipe) first
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allocate_bcd_task = asyncio.create_task(_allocate_and_wait(dealloc_bcd_event, descr_b, descr_c, descr_d))
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allocate_e_task = asyncio.create_task(_allocate_and_wait(dealloc_e_event, descr_e)) # doesn't fit
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await asyncio.wait({allocate_e_task, allocate_bcd_task}, return_when=asyncio.ALL_COMPLETED)
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alloc_process2 = mp.context.ForkProcess(target=lambda: asyncio.run(_allocate_bcde()), daemon=True)
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alloc_process2.start()
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assert cache.current_size_bytes == 0
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alloc_event.set()
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(handle_a,) = pipe_receiver.recv()
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handle_b, handle_c, handle_d = pipe_receiver.recv()
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with cache.use_cache(handle_a) as (tensor_a,):
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assert tensor_a.dtype == torch.uint8
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tensor_a[2:5] = torch.tensor((42, 43, 44))
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with cache.use_cache(handle_a, handle_b, handle_d) as (tensor_a, tensor_b, tensor_d):
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assert tensor_b.dtype == torch.float64 and tensor_b.numel() == 1 and tensor_b.ndim == 0
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assert tensor_d.dtype == torch.int64 and tensor_d.numel() == 0
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tensor_a += 1
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tensor_b[...] = -1.337
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assert cache.current_size_bytes == 809 # this checks a,b,c,d are allocated but b still awaits memory
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dealloc_bcd_event.set()
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await asyncio.sleep(0.1)
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assert cache.current_size_bytes == 768 # only tensor a should be allocated
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with pytest.raises(KeyError):
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with cache.use_cache(handle_a, handle_b):
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pass # one of handles (c) is deallocated
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with pytest.raises(KeyError):
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with cache.use_cache(handle_d):
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pass # handle_d is deallocated correctly, even though it is never used
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with cache.use_cache(handle_a) as (tensor_a,):
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assert tuple(tensor_a[2:5]) == (43, 44, 45)
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dealloc_a_event.set()
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(handle_e,) = pipe_receiver.recv() # e can finally be allocated
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await asyncio.sleep(0.1)
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assert cache.current_size_bytes == 1536 # tensor e should finally be able to allocate
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with pytest.raises(KeyError):
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with cache.use_cache(handle_a):
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pass # tensor a is no longer allocated
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with cache.use_cache(handle_e) as (tensor_e,):
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assert tensor_e.dtype == torch.bfloat16 and tensor_e.shape == (96, 8)
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dealloc_e_event.set()
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await asyncio.sleep(0.1)
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assert cache.current_size_bytes == 1792 # only tensor f is still allocated
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dealloc_f_event.set()
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alloc_process1.join()
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alloc_process2.join()
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await asyncio.sleep(0.1)
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assert cache.current_size_bytes == 0
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assert cache.current_size_bytes == 0
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assert alloc_process1.exitcode == 0, "allocation process 1 failed or did not finish, see stderr for details"
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assert alloc_process2.exitcode == 0, "allocation process 2 failed or did not finish, see stderr for details"
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