[V1] Zero-copy tensor/ndarray serialization/transmission

tests/v1/test_serial_utils.py

@@ -0,0 +1,80 @@
+# SPDX-License-Identifier: Apache-2.0
+from collections import UserDict
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+
+from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
+
+
+class UnrecognizedType(UserDict):
+
+    def __init__(self, an_int: int):
+        super().__init__()
+        self.an_int = an_int
+
+
+@dataclass
+class MyType:
+    tensor1: torch.Tensor
+    a_string: str
+    list_of_tensors: list[torch.Tensor]
+    numpy_array: np.ndarray
+    unrecognized: UnrecognizedType
+
+
+def test_encode_decode():
+    """Test encode/decode loop with zero-copy tensors."""
+
+    obj = MyType(
+        tensor1=torch.randint(low=0,
+                              high=100,
+                              size=(1024, ),
+                              dtype=torch.int32),
+        a_string="hello",
+        list_of_tensors=[
+            torch.rand((1, 10), dtype=torch.float32),
+            torch.rand((3, 5, 4000), dtype=torch.float64),
+            torch.tensor(1984),  # test scalar too
+        ],
+        numpy_array=np.arange(512),
+        unrecognized=UnrecognizedType(33),
+    )
+
+    encoder = MsgpackEncoder()
+    decoder = MsgpackDecoder(MyType)
+
+    encoded = encoder.encode(obj)
+
+    # There should be the main buffer + 2 large tensor buffers
+    # + 1 large numpy array. "large" is <= 256 bytes.
+    # The two small tensors are encoded inline.
+    assert len(encoded) == 4
+
+    decoded: MyType = decoder.decode(encoded)
+
+    assert_equal(decoded, obj)
+
+    # Test encode_into case
+
+    preallocated = bytearray()
+
+    encoded2 = encoder.encode_into(obj, preallocated)
+
+    assert len(encoded2) == 4
+    assert encoded2[0] is preallocated
+
+    decoded2: MyType = decoder.decode(encoded2)
+
+    assert_equal(decoded2, obj)
+
+
+def assert_equal(obj1: MyType, obj2: MyType):
+    assert torch.equal(obj1.tensor1, obj2.tensor1)
+    assert obj1.a_string == obj2.a_string
+    assert all(
+        torch.equal(a, b)
+        for a, b in zip(obj1.list_of_tensors, obj2.list_of_tensors))
+    assert np.array_equal(obj1.numpy_array, obj2.numpy_array)
+    assert obj1.unrecognized.an_int == obj2.unrecognized.an_int

vllm/v1/engine/core.py

@@ -490,14 +490,14 @@ def process_input_socket(self, input_path: str, engine_index: int):
 
             while True:
                 # (RequestType, RequestData)
-                type_frame, data_frame = socket.recv_multipart(copy=False)
+                type_frame, *data_frames = socket.recv_multipart(copy=False)
                 request_type = EngineCoreRequestType(bytes(type_frame.buffer))
 
                 # Deserialize the request data.
                 decoder = add_request_decoder if (
                     request_type
                     == EngineCoreRequestType.ADD) else generic_decoder
-                request = decoder.decode(data_frame.buffer)
+                request = decoder.decode(data_frames)
 
                 # Push to input queue for core busy loop.
                 self.input_queue.put_nowait((request_type, request))
@@ -514,8 +514,8 @@ def process_output_socket(self, output_path: str, engine_index: int):
             while True:
                 outputs = self.output_queue.get()
                 outputs.engine_index = engine_index
-                encoder.encode_into(outputs, buffer)
-                socket.send(buffer, copy=False)
+                buffers = encoder.encode_into(outputs, buffer)
+                socket.send_multipart(buffers, copy=False)
 
 
 ENGINE_PAUSED_OUTPUTS = EngineCoreOutputs(engine_paused=True)

vllm/v1/engine/core_client.py

@@ -26,7 +26,7 @@
                             EngineCoreRequestType, UtilityOutput)
 from vllm.v1.engine.core import EngineCore, EngineCoreProc
 from vllm.v1.executor.abstract import Executor
-from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
+from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder, bytestr
 from vllm.v1.utils import BackgroundProcHandle
 
 logger = init_logger(__name__)
@@ -505,8 +505,8 @@ def process_outputs_socket():
                         # shutdown signal, exit thread.
                         break
 
-                    frame = out_socket.recv(copy=False)
-                    outputs = decoder.decode(frame.buffer)
+                    frames = out_socket.recv_multipart(copy=False)
+                    outputs = decoder.decode(frames)
                     if outputs.utility_output:
                         _process_utility_output(outputs.utility_output,
                                                 utility_results)
@@ -529,7 +529,7 @@ def get_output(self) -> EngineCoreOutputs:
     def _send_input(self, request_type: EngineCoreRequestType, request: Any):
         # (Identity, RequestType, SerializedRequest)
         msg = (self.core_engine.identity, request_type.value,
-               self.encoder.encode(request))
+               *self.encoder.encode(request))
         self.input_socket.send_multipart(msg, copy=False)
 
     def call_utility(self, method: str, *args) -> Any:
@@ -633,8 +633,8 @@ def _ensure_output_queue_task(self):
 
         async def process_outputs_socket():
             while True:
-                (frame, ) = await output_socket.recv_multipart(copy=False)
-                outputs: EngineCoreOutputs = decoder.decode(frame.buffer)
+                frames = await output_socket.recv_multipart(copy=False)
+                outputs: EngineCoreOutputs = decoder.decode(frames)
                 if outputs.utility_output:
                     _process_utility_output(outputs.utility_output,
                                             utility_results)
@@ -666,12 +666,12 @@ def _send_input(self,
         if engine is None:
             engine = self.core_engine
 
-        message = (request_type.value, self.encoder.encode(request))
+        message = (request_type.value, *self.encoder.encode(request))
         return self._send_input_message(message, engine)
 
-    def _send_input_message(self, message: tuple[bytes, bytes],
+    def _send_input_message(self, message: tuple[bytestr, ...],
                             engine: CoreEngine) -> Awaitable[None]:
-        message = (engine.identity, ) + message  # type: ignore[assignment]
+        message = (engine.identity, ) + message
         return self.input_socket.send_multipart(message, copy=False)
 
     async def call_utility_async(self, method: str, *args) -> Any:
@@ -684,8 +684,8 @@ async def _call_utility_async(self, method: str, *args,
         call_id = uuid.uuid1().int >> 64
         future = asyncio.get_running_loop().create_future()
         self.utility_results[call_id] = future
-        message = (EngineCoreRequestType.UTILITY.value,
-                   self.encoder.encode((call_id, method, args)))
+        message = (EngineCoreRequestType.UTILITY.value, *self.encoder.encode(
+            (call_id, method, args)))
         await self._send_input_message(message, engine)
         self._ensure_output_queue_task()
         return await future
@@ -760,7 +760,7 @@ def __init__(self, vllm_config: VllmConfig, executor_class: type[Executor],
 
         # Control message used for triggering dp idle mode loop.
         self.start_dp_msg = (EngineCoreRequestType.START_DP.value,
-                             self.encoder.encode(None))
+                             *self.encoder.encode(None))
 
         self.num_engines_running = 0
         self.reqs_in_flight: dict[str, CoreEngine] = {}
@@ -794,7 +794,7 @@ async def add_request_async(self, request: EngineCoreRequest) -> None:
         # tokenized.
         request.prompt = None
 
-        msg = (EngineCoreRequestType.ADD.value, self.encoder.encode(request))
+        msg = (EngineCoreRequestType.ADD.value, *self.encoder.encode(request))
 
         chosen_engine = self.get_core_engine_for_request()
         self.reqs_in_flight[request.request_id] = chosen_engine

vllm/v1/serial_utils.py

@@ -1,61 +1,140 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import pickle
+from collections.abc import Sequence
+from inspect import isclass
 from types import FunctionType
-from typing import Any, Optional
+from typing import Any, Optional, Union
 
 import cloudpickle
+import numpy as np
 import torch
+import zmq
 from msgspec import msgpack
 
-CUSTOM_TYPE_TENSOR = 1
-CUSTOM_TYPE_PICKLE = 2
-CUSTOM_TYPE_CLOUDPICKLE = 3
+CUSTOM_TYPE_PICKLE = 1
+CUSTOM_TYPE_CLOUDPICKLE = 2
 
+# TODO calibrate this size
+INLINE_BUF_SIZE_THRESHOLD = 256
 
-class MsgpackEncoder:
-    """Encoder with custom torch tensor serialization."""
+bytestr = Union[bytes, bytearray, memoryview, zmq.Frame]
 
-    def __init__(self):
-        self.encoder = msgpack.Encoder(enc_hook=custom_enc_hook)
 
-    def encode(self, obj: Any) -> bytes:
-        return self.encoder.encode(obj)
+class MsgpackEncoder:
+    """Encoder with custom torch tensor and numpy array serialization.
 
-    def encode_into(self, obj: Any, buf: bytearray) -> None:
-        self.encoder.encode_into(obj, buf)
+    Note that unlike vanilla `msgspec` Encoders, this interface is generally
+    not thread-safe when encoding tensors / numpy arrays.
+    """
+
+    def __init__(self):
+        self.encoder = msgpack.Encoder(enc_hook=self.enc_hook)
+        # This is used as a local stash of buffers that we can then access from
+        # our custom `msgspec` hook, `enc_hook`. We don't have a way to
+        # pass custom data to the hook otherwise.
+        self.aux_buffers: Optional[list[bytestr]] = None
+
+    def encode(self, obj: Any) -> Sequence[bytestr]:
+        try:
+            self.aux_buffers = bufs = [b'']
+            bufs[0] = self.encoder.encode(obj)
+            # This `bufs` list allows us to collect direct pointers to backing
+            # buffers of tensors and np arrays, and return them along with the
+            # top-level encoded buffer instead of copying their data into the
+            # new buffer.
+            return bufs
+        finally:
+            self.aux_buffers = None
+
+    def encode_into(self, obj: Any, buf: bytearray) -> Sequence[bytestr]:
+        try:
+            self.aux_buffers = [buf]
+            bufs = self.aux_buffers
+            self.encoder.encode_into(obj, buf)
+            return bufs
+        finally:
+            self.aux_buffers = None
+
+    def enc_hook(self, obj: Any) -> Any:
+        if isinstance(obj, torch.Tensor):
+            return self._encode_ndarray(obj.numpy())
+
+        # Fall back to pickle for object or void kind ndarrays.
+        if isinstance(obj, np.ndarray) and obj.dtype.kind not in ('O', 'V'):
+            return self._encode_ndarray(obj)
+
+        if isinstance(obj, FunctionType):
+            # `pickle` is generally faster than cloudpickle, but can have
+            # problems serializing methods.
+            return msgpack.Ext(CUSTOM_TYPE_CLOUDPICKLE, cloudpickle.dumps(obj))
+
+        return msgpack.Ext(CUSTOM_TYPE_PICKLE,
+                           pickle.dumps(obj, protocol=pickle.HIGHEST_PROTOCOL))
+
+    def _encode_ndarray(
+        self, obj: np.ndarray
+    ) -> tuple[str, tuple[int, ...], Union[int, memoryview]]:
+        assert self.aux_buffers is not None
+        if not obj.shape or obj.nbytes < INLINE_BUF_SIZE_THRESHOLD:
+            # Encode small arrays and scalars inline.
+            data = obj.data
+        else:
+            # Otherwise encode index of backing buffer.
+            obj = np.ascontiguousarray(obj)
+            data = len(self.aux_buffers)
+            self.aux_buffers.append(obj.data)
+        # We serialize the ndarray as a tuple of native types.
+        # The data is either inlined if small, or an index into a list of
+        # backing buffers that we've stashed in `aux_buffers`.
+        return obj.dtype.str, obj.shape, data
 
 
 class MsgpackDecoder:
-    """Decoder with custom torch tensor serialization."""
+    """Decoder with custom torch tensor and numpy array serialization.
+
+    Note that unlike vanilla `msgspec` Decoders, this interface is generally
+    not thread-safe when encoding tensors / numpy arrays.
+    """
 
     def __init__(self, t: Optional[Any] = None):
         args = () if t is None else (t, )
-        self.decoder = msgpack.Decoder(*args, ext_hook=custom_ext_hook)
-
-    def decode(self, obj: Any):
-        return self.decoder.decode(obj)
-
-
-def custom_enc_hook(obj: Any) -> Any:
-    if isinstance(obj, torch.Tensor):
-        # NOTE(rob): it is fastest to use numpy + pickle
-        # when serializing torch tensors.
-        # https://gist.github.com/tlrmchlsmth/8067f1b24a82b6e2f90450e7764fa103 # noqa: E501
-        return msgpack.Ext(CUSTOM_TYPE_TENSOR, pickle.dumps(obj.numpy()))
-
-    if isinstance(obj, FunctionType):
-        return msgpack.Ext(CUSTOM_TYPE_CLOUDPICKLE, cloudpickle.dumps(obj))
-
-    return msgpack.Ext(CUSTOM_TYPE_PICKLE, pickle.dumps(obj))
-
-
-def custom_ext_hook(code: int, data: memoryview) -> Any:
-    if code == CUSTOM_TYPE_TENSOR:
-        return torch.from_numpy(pickle.loads(data))
-    if code == CUSTOM_TYPE_PICKLE:
-        return pickle.loads(data)
-    if code == CUSTOM_TYPE_CLOUDPICKLE:
-        return cloudpickle.loads(data)
-
-    raise NotImplementedError(f"Extension type code {code} is not supported")
+        self.decoder = msgpack.Decoder(*args,
+                                       ext_hook=self.ext_hook,
+                                       dec_hook=self.dec_hook)
+        self.aux_buffers: Sequence[bytestr] = ()
+
+    def decode(self, bufs: Union[bytestr, Sequence[bytestr]]) -> Any:
+        if isinstance(bufs, (bytes, bytearray, memoryview, zmq.Frame)):
+            # TODO - This check can become `isinstance(bufs, bytestr)`
+            # as of Python 3.10.
+            return self.decoder.decode(bufs)
+
+        self.aux_buffers = bufs
+        try:
+            return self.decoder.decode(bufs[0])
+        finally:
+            self.aux_buffers = ()
+
+    def dec_hook(self, t: type, obj: Any) -> Any:
+        # Given native types in `obj`, convert to type `t`.
+        if isclass(t):
+            if issubclass(t, np.ndarray):
+                return self._decode_ndarray(obj)
+            if issubclass(t, torch.Tensor):
+                return torch.from_numpy(self._decode_ndarray(obj))
+        return obj
+
+    def _decode_ndarray(self, arr: Any) -> np.ndarray:
+        dtype, shape, data = arr
+        buffer = self.aux_buffers[data] if isinstance(data, int) else data
+        return np.ndarray(buffer=buffer, dtype=np.dtype(dtype), shape=shape)
+
+    def ext_hook(self, code: int, data: memoryview) -> Any:
+        if code == CUSTOM_TYPE_PICKLE:
+            return pickle.loads(data)
+        if code == CUSTOM_TYPE_CLOUDPICKLE:
+            return cloudpickle.loads(data)
+
+        raise NotImplementedError(
+            f"Extension type code {code} is not supported")