Just-in-time deserialization (#353)

Co-authored-by: Peter Andreas Entschev <[email protected]>

* Initial implementing of ObjectProxy

* Added basic tests of ObjectProxy

* Implemented some more proxy attributes

* Added spilling of proxy object optional

* Re-added dask_serialize for DeviceSerialized

* Added support of __array__

* Added __sizeof__

* Added some spill_proxy tests in test_device_host_file.py

* Checking len() instead of .size()

* Added dispatch support of hash_object_dispatch and group_split_dispatch

* Added "*args, **kwargs" to dispatch of ObjectProxy

* Added dispatch of make_scalar

* Added dispatch of concat_dispatch

* meta.yaml: added pandas dependency

* meta.yaml: depend on dask (not only dask-core)

* Added jit-unspill worker option

* meta.yaml: removed pandas

* Using explicit args for the dispatch functions

* ObjectProxy._obj_pxy_serialize(): takes serializers

* serializers replaces is_serialized

* Supporting cuda serializers

* Added a lot of operators

* fixed typos

* Support and test of a proxy object of a proxy object

* test_spilling_local_cuda_cluster(): added some extra checks

* Added _obj_pxy_is_cuda_object()

* asproxy(): added subclass argument

* fixed type in test_spilling_local_cuda_cluster check

* Added test of communicating proxy objects

* Making ObjectProxy threadsafe

* renamed ObjectProxy => ProxyObject

* Never re-serialize proxy objects

* Test: setting device_memory_limit="1B" to force serialization

* test: added an explicit client shutdown

* Added some str/repr tests

* added some more checks in test_proxy_object_of_numpy

* ProxyObject: added docs

* added unproxy()

* Added ValueError when serializers isn't specified

* Style and spelling fixes

* ProxyObject.__sizeof__(): use dask.sizeof()

* Serializer: convert to tuples before comparing

conda/recipes/dask-cuda/meta.yaml

@@ -24,7 +24,7 @@ requirements:
     - setuptools
   run:
     - python x.x
-    - dask-core >=2.4.0
+    - dask >=2.4.0
     - distributed >=2.18.0
     - pynvml >=8.0.3
     - numpy >=1.16.0

dask_cuda/cli/dask_cuda_worker.py

@@ -191,6 +191,11 @@
     "InfiniBand only and will still cause unpredictable errors if not _ALL_ "
     "interfaces are connected and properly configured.",
 )
+@click.option(
+    "--enable-jit-unspill/--disable-jit-unspill",
+    default=None,  # If not specified, use Dask config
+    help="Enable just-in-time unspilling",
+)
 def main(
     scheduler,
     host,
@@ -218,6 +223,7 @@ def main(
     enable_nvlink,
     enable_rdmacm,
     net_devices,
+    enable_jit_unspill,
     **kwargs,
 ):
     if tls_ca_file and tls_cert and tls_key:
@@ -252,6 +258,7 @@ def main(
         enable_nvlink,
         enable_rdmacm,
         net_devices,
+        enable_jit_unspill,
         **kwargs,
     )
 

dask_cuda/cuda_worker.py

@@ -71,6 +71,7 @@ def __init__(
         enable_nvlink=False,
         enable_rdmacm=False,
         net_devices=None,
+        jit_unspill=None,
         **kwargs,
     ):
         # Required by RAPIDS libraries (e.g., cuDF) to ensure no context
@@ -177,6 +178,11 @@ def del_pid_file():
             cuda_device_index=0,
         )
 
+        if jit_unspill is None:
+            self.jit_unspill = dask.config.get("jit-unspill", default=False)
+        else:
+            self.jit_unspill = jit_unspill
+
         self.nannies = [
             t(
                 scheduler,
@@ -216,6 +222,7 @@ def del_pid_file():
                         ),
                         "memory_limit": memory_limit,
                         "local_directory": local_directory,
+                        "jit_unspill": self.jit_unspill,
                     },
                 ),
                 **kwargs,

dask_cuda/device_host_file.py

@@ -16,15 +16,14 @@
 from distributed.utils import nbytes
 from distributed.worker import weight
 
+from . import proxy_object
 from .is_device_object import is_device_object
 from .utils import nvtx_annotate
 
 
 class DeviceSerialized:
     """ Store device object on the host
-
     This stores a device-side object as
-
     1.  A msgpack encodable header
     2.  A list of `bytes`-like objects (like NumPy arrays)
         that are in host memory
@@ -66,6 +65,27 @@ def host_to_device(s: DeviceSerialized) -> object:
     return deserialize(s.header, s.frames)
 
 
+@nvtx_annotate("SPILL_D2H", color="red", domain="dask_cuda")
+def pxy_obj_device_to_host(obj: object) -> proxy_object.ProxyObject:
+    try:
+        # Never re-serialize proxy objects.
+        if obj._obj_pxy["serializers"] is None:
+            return obj
+    except (KeyError, AttributeError):
+        pass
+
+    # Notice, both the "dask" and the "pickle" serializer will
+    # spill `obj` to main memory.
+    return proxy_object.asproxy(obj, serializers=["dask", "pickle"])
+
+
+@nvtx_annotate("SPILL_H2D", color="green", domain="dask_cuda")
+def pxy_obj_host_to_device(s: proxy_object.ProxyObject) -> object:
+    # Notice, we do _not_ deserialize at this point. The proxy
+    # object automatically deserialize just-in-time.
+    return s
+
+
 class DeviceHostFile(ZictBase):
     """ Manages serialization/deserialization of objects.
 
@@ -86,10 +106,16 @@ class DeviceHostFile(ZictBase):
         implies no spilling to disk.
     local_directory: path
         Path where to store serialized objects on disk
+    jit_unspill: bool
+        If True, enable just-in-time unspilling (see proxy_object.ProxyObject).
     """
 
     def __init__(
-        self, device_memory_limit=None, memory_limit=None, local_directory=None,
+        self,
+        device_memory_limit=None,
+        memory_limit=None,
+        local_directory=None,
+        jit_unspill=False,
     ):
         if local_directory is None:
             local_directory = dask.config.get("temporary-directory") or os.getcwd()
@@ -115,7 +141,14 @@ def __init__(
 
         self.device_keys = set()
         self.device_func = dict()
-        self.device_host_func = Func(device_to_host, host_to_device, self.host_buffer)
+        if jit_unspill:
+            self.device_host_func = Func(
+                pxy_obj_device_to_host, pxy_obj_host_to_device, self.host_buffer
+            )
+        else:
+            self.device_host_func = Func(
+                device_to_host, host_to_device, self.host_buffer
+            )
         self.device_buffer = Buffer(
             self.device_func, self.device_host_func, device_memory_limit, weight=weight
         )

dask_cuda/local_cuda_cluster.py

@@ -92,6 +92,8 @@ class LocalCUDACluster(LocalCluster):
         but in that case with default (non-managed) memory type.
         WARNING: managed memory is currently incompatible with NVLink, trying
         to enable both will result in an exception.
+    jit_unspill: bool
+        If True, enable just-in-time unspilling (see proxy_object.ProxyObject).
 
     Examples
     --------
@@ -133,6 +135,7 @@ def __init__(
         ucx_net_devices=None,
         rmm_pool_size=None,
         rmm_managed_memory=False,
+        jit_unspill=None,
         **kwargs,
     ):
         # Required by RAPIDS libraries (e.g., cuDF) to ensure no context
@@ -182,6 +185,11 @@ def __init__(
                 "Processes are necessary in order to use multiple GPUs with Dask"
             )
 
+        if jit_unspill is None:
+            self.jit_unspill = dask.config.get("jit-unspill", default=False)
+        else:
+            self.jit_unspill = jit_unspill
+
         if data is None:
             data = (
                 DeviceHostFile,
@@ -191,6 +199,7 @@ def __init__(
                     "local_directory": local_directory
                     or dask.config.get("temporary-directory")
                     or os.getcwd(),
+                    "jit_unspill": self.jit_unspill,
                 },
             )