|
|
@@ -0,0 +1,132 @@
|
|
|
+import random
|
|
|
+from typing import Optional
|
|
|
+
|
|
|
+import pytest
|
|
|
+import torch
|
|
|
+from hivemind import TensorDescriptor
|
|
|
+
|
|
|
+from petals.server.memory_cache import MemoryCache, AllocationFailed
|
|
|
+import asyncio
|
|
|
+from petals.utils.misc import get_size_in_bytes
|
|
|
+import multiprocessing as mp
|
|
|
+import pytest_asyncio # make sure the module exists; otherwise the test will be skipped
|
|
|
+
|
|
|
+
|
|
|
+def _make_tensor_descriptor(num_bytes: int, dtype: Optional[torch.dtype] = None):
|
|
|
+ if dtype is None:
|
|
|
+ dtype = random.choice((torch.int64, torch.int8, torch.uint8, torch.float32, torch.bfloat16, torch.bool))
|
|
|
+ elem_size_bytes = get_size_in_bytes(dtype)
|
|
|
+ descr = TensorDescriptor.from_tensor(torch.empty((num_bytes // elem_size_bytes,), dtype=dtype))
|
|
|
+ return descr
|
|
|
+
|
|
|
+
|
|
|
+@pytest.mark.asyncio
|
|
|
+async def test_cache_usage():
|
|
|
+ cache = MemoryCache(max_size_bytes=2048)
|
|
|
+ alloc_event, dealloc_e_event, dealloc_bcd_event, dealloc_a_event = mp.Event(), mp.Event(), mp.Event(), mp.Event()
|
|
|
+ pipe_receiver, pipe_sender = mp.Pipe(duplex=False)
|
|
|
+ with pytest.raises(AssertionError):
|
|
|
+ async with cache.allocate_cache(_make_tensor_descriptor(123)):
|
|
|
+ pass # fails because cache must be allocated from another process
|
|
|
+
|
|
|
+ descr_a = TensorDescriptor.from_tensor(torch.empty(768, dtype=torch.uint8)) # 768 bytes
|
|
|
+ descr_b = TensorDescriptor.from_tensor(torch.empty((), dtype=torch.float64)) # 8 bytes
|
|
|
+ descr_c = TensorDescriptor.from_tensor(torch.empty((33, ), dtype=torch.bool)) # 33 bytes
|
|
|
+ descr_d = TensorDescriptor.from_tensor(torch.empty((0, ), dtype=torch.int64)) # 0 bytes
|
|
|
+ descr_e = TensorDescriptor.from_tensor(torch.empty((96, 8), dtype=torch.bfloat16)) # 1536 bytes
|
|
|
+ descr_f = TensorDescriptor.from_tensor(torch.empty((1792,), dtype=torch.uint8)) # 1792 bytes
|
|
|
+ descr_g = TensorDescriptor.from_tensor(torch.empty((1793,), dtype=torch.uint8)) # 1792 bytes
|
|
|
+
|
|
|
+ async def _allocate_and_wait(dealloc_event, *descrs, timeout=None):
|
|
|
+ loop = asyncio.get_event_loop()
|
|
|
+ async with cache.allocate_cache(*descrs, timeout=timeout) as handles:
|
|
|
+ pipe_sender.send(handles)
|
|
|
+ await loop.run_in_executor(None, dealloc_event.wait)
|
|
|
+
|
|
|
+ async def _allocate_af():
|
|
|
+ alloc_event.wait()
|
|
|
+ print("BEGAN AF")
|
|
|
+ try:
|
|
|
+ async with cache.allocate_cache(descr_g):
|
|
|
+ allocate_f_task = asyncio.create_task(_allocate_and_wait(mp.Event(), descr_f)) # klogs the cache
|
|
|
+ print("CANCELLED")
|
|
|
+ raise asyncio.CancelledError()
|
|
|
+ except asyncio.CancelledError:
|
|
|
+ pass
|
|
|
+ allocate_f_task.cancel() # unklog the cache
|
|
|
+
|
|
|
+ allocate_a_task = asyncio.create_task(_allocate_and_wait(dealloc_a_event, descr_a))
|
|
|
+ await allocate_a_task
|
|
|
+
|
|
|
+ alloc_process1 = mp.Process(target=lambda: asyncio.run(_allocate_af()), daemon=True)
|
|
|
+ alloc_process1.start()
|
|
|
+
|
|
|
+ async def _allocate_bcde():
|
|
|
+ await asyncio.sleep(0.2) # ensure that the other tensor is always allocated (and sent through pipe) first
|
|
|
+ print("BEGAN BCDE")
|
|
|
+ allocate_bcd_task = asyncio.create_task(_allocate_and_wait(dealloc_bcd_event, descr_b, descr_c, descr_d))
|
|
|
+ allocate_e_task = asyncio.create_task(_allocate_and_wait(dealloc_e_event, descr_e)) # doesn't fit
|
|
|
+ await asyncio.wait({allocate_e_task, allocate_bcd_task}, return_when=asyncio.ALL_COMPLETED)
|
|
|
+
|
|
|
+ alloc_process2 = mp.Process(target=lambda: asyncio.run(_allocate_bcde()), daemon=True)
|
|
|
+ alloc_process2.start()
|
|
|
+ assert cache.current_size_bytes == 0
|
|
|
+ alloc_event.set()
|
|
|
+ handle_a, = pipe_receiver.recv()
|
|
|
+
|
|
|
+ handle_b, handle_c, handle_d = pipe_receiver.recv()
|
|
|
+
|
|
|
+ with cache.use_cache(handle_a) as (tensor_a,):
|
|
|
+ assert tensor_a.dtype == torch.uint8
|
|
|
+ tensor_a[2:5] = torch.tensor((42, 43, 44))
|
|
|
+
|
|
|
+ with cache.use_cache(handle_a, handle_b, handle_d) as (tensor_a, tensor_b, tensor_d):
|
|
|
+ assert tensor_b.dtype == torch.float64 and tensor_b.numel() == 1 and tensor_b.ndim == 0
|
|
|
+ assert tensor_d.dtype == torch.int64 and tensor_d.numel() == 0
|
|
|
+ tensor_a += 1
|
|
|
+ tensor_b[...] = -1.337
|
|
|
+ assert cache.current_size_bytes == 809 # this checks a,b,c,d are allocated but b still awaits memory
|
|
|
+
|
|
|
+ dealloc_bcd_event.set()
|
|
|
+ await asyncio.sleep(0.1)
|
|
|
+ assert cache.current_size_bytes == 768 # only tensor a is allocated
|
|
|
+ with pytest.raises(KeyError):
|
|
|
+ with cache.use_cache(handle_a, handle_b):
|
|
|
+ pass # one of handles (c) is deallocated
|
|
|
+ with pytest.raises(KeyError):
|
|
|
+ with cache.use_cache(handle_d):
|
|
|
+ pass # handle_e is deallocated, even though it is never used
|
|
|
+ with cache.use_cache(handle_a) as (tensor_a,):
|
|
|
+ assert tuple(tensor_a[2:5]) == (43, 44, 45)
|
|
|
+
|
|
|
+ dealloc_a_event.set()
|
|
|
+ handle_e, = pipe_receiver.recv() # e can finally be allocated
|
|
|
+ assert cache.current_size_bytes == 1536 # tensor e should finally be able to allocate
|
|
|
+
|
|
|
+ with pytest.raises(KeyError):
|
|
|
+ with cache.use_cache(handle_a):
|
|
|
+ pass # tensor a is no longer allocated
|
|
|
+ with cache.use_cache(handle_e) as (tensor_e,):
|
|
|
+ assert tensor_e.dtype == torch.bfloat16 and tensor_e.shape == (96, 8)
|
|
|
+
|
|
|
+ dealloc_e_event.set()
|
|
|
+ alloc_process1.join(1)
|
|
|
+ alloc_process2.join(1)
|
|
|
+ assert cache.current_size_bytes == 0
|
|
|
+ assert alloc_process1.exitcode == 0, "allocation process 1 failed or did not finish, see stderr for details"
|
|
|
+ assert alloc_process2.exitcode == 0, "allocation process 2 failed or did not finish, see stderr for details"
|
|
|
+
|
|
|
+ # cache.runtime_pid += 1 # pretend we're another process
|
|
|
+ # async with cache.allocate_cache(_make_tensor_descriptor(768)) as a:
|
|
|
+ # pass
|
|
|
+ #
|
|
|
+ #
|
|
|
+ # async with cache.allocate_cache(_make_tensor_descriptor(768)):
|
|
|
+ # async with cache.allocate_cache(_make_tensor_descriptor(1024)):
|
|
|
+ # async with cache.allocate_cache(_make_tensor_descriptor(512), _make_tensor_descriptor(64)):
|
|
|
+ # async with cache.allocate_cache(_make_tensor_descriptor(1536)):
|
|
|
+ # with pytest.raises(TimeoutError):
|
|
|
+ # async with cache.allocate_cache(_make_tensor_descriptor(256), ):
|
|
|
+ # pass
|
|
|
+ # async with cache.allocate_cache(_make_tensor_descriptor(192)):
|
|
|
+ # pass
|
Your Name