Serialize tensors using int8 views (#16866)

Signed-off-by: Staszek Pasko <staszek@gmail.com>
Co-authored-by: Nick Hill <nhill@redhat.com>
This commit is contained in:
Staszek Paśko
2025-04-19 19:28:34 +02:00
committed by GitHub
parent 682e0b6d2f
commit 87aaadef73
2 changed files with 48 additions and 13 deletions

View File

@ -47,6 +47,10 @@ def test_encode_decode():
torch.rand((1, 10), dtype=torch.float32),
torch.rand((3, 5, 4000), dtype=torch.float64),
torch.tensor(1984), # test scalar too
# Make sure to test bf16 which numpy doesn't support.
torch.rand((3, 5, 1000), dtype=torch.bfloat16),
torch.tensor([float("-inf"), float("inf")] * 1024,
dtype=torch.bfloat16),
],
numpy_array=np.arange(512),
unrecognized=UnrecognizedType(33),
@ -64,7 +68,7 @@ def test_encode_decode():
# There should be the main buffer + 4 large tensor buffers
# + 1 large numpy array. "large" is <= 512 bytes.
# The two small tensors are encoded inline.
assert len(encoded) == 6
assert len(encoded) == 8
decoded: MyType = decoder.decode(encoded)
@ -76,7 +80,7 @@ def test_encode_decode():
encoded2 = encoder.encode_into(obj, preallocated)
assert len(encoded2) == 6
assert len(encoded2) == 8
assert encoded2[0] is preallocated
decoded2: MyType = decoder.decode(encoded2)
@ -114,15 +118,15 @@ def test_multimodal_kwargs():
total_len = sum(memoryview(x).cast("B").nbytes for x in encoded)
# expected total encoding length, should be 44536, +-20 for minor changes
assert total_len >= 44516 and total_len <= 44556
# expected total encoding length, should be 44559, +-20 for minor changes
assert total_len >= 44539 and total_len <= 44579
decoded: MultiModalKwargs = decoder.decode(encoded).mm[0]
assert all(nested_equal(d[k], decoded[k]) for k in d)
def test_multimodal_items_by_modality():
e1 = MultiModalFieldElem("audio", "a0", torch.zeros(1000,
dtype=torch.int16),
e1 = MultiModalFieldElem("audio", "a0",
torch.zeros(1000, dtype=torch.bfloat16),
MultiModalBatchedField())
e2 = MultiModalFieldElem(
"video",

View File

@ -80,7 +80,7 @@ class MsgpackEncoder:
def enc_hook(self, obj: Any) -> Any:
if isinstance(obj, torch.Tensor):
return self._encode_ndarray(obj.numpy())
return self._encode_tensor(obj)
# Fall back to pickle for object or void kind ndarrays.
if isinstance(obj, np.ndarray) and obj.dtype.kind not in ('O', 'V'):
@ -133,9 +133,27 @@ class MsgpackEncoder:
# backing buffers that we've stashed in `aux_buffers`.
return obj.dtype.str, obj.shape, data
def _encode_tensor(
self, obj: torch.Tensor
) -> tuple[str, tuple[int, ...], Union[int, memoryview]]:
assert self.aux_buffers is not None
# this creates a copy of the tensor if it's not already contiguous
obj = obj.contiguous()
# view the tensor as a 1D array of bytes
arr = obj.view((obj.numel(), )).view(torch.uint8).numpy()
if obj.nbytes < self.size_threshold:
# Smaller tensors are encoded inline, just like ndarrays.
data = msgpack.Ext(CUSTOM_TYPE_RAW_VIEW, arr.data)
else:
# Otherwise encode index of backing buffer to avoid copy.
data = len(self.aux_buffers)
self.aux_buffers.append(arr.data)
dtype = str(obj.dtype)[6:] # remove 'torch.' prefix
return dtype, obj.shape, data
def _encode_nested_tensors(self, nt: NestedTensors) -> Any:
if isinstance(nt, torch.Tensor):
return self._encode_ndarray(nt.numpy())
return self._encode_tensor(nt)
if isinstance(nt, (int, float)):
# Although it violates NestedTensors type, MultiModalKwargs
# values are sometimes floats.
@ -186,7 +204,7 @@ class MsgpackDecoder:
if issubclass(t, np.ndarray):
return self._decode_ndarray(obj)
if issubclass(t, torch.Tensor):
return torch.from_numpy(self._decode_ndarray(obj))
return self._decode_tensor(obj)
if issubclass(t, MultiModalKwargs):
if isinstance(obj, list):
return MultiModalKwargs.from_items(
@ -199,11 +217,24 @@ class MsgpackDecoder:
def _decode_ndarray(self, arr: Any) -> np.ndarray:
dtype, shape, data = arr
# Copy from inline representation, otherwise Torch is unhappy since
# the returned memory is non-writeable.
# zero-copy decode. We assume the ndarray will not be kept around,
# as it now locks the whole received message buffer in memory.
buffer = self.aux_buffers[data] if isinstance(data, int) else data
return np.ndarray(buffer=buffer, dtype=np.dtype(dtype), shape=shape)
def _decode_tensor(self, arr: Any) -> torch.Tensor:
dtype, shape, data = arr
# Copy from inline representation, to decouple the memory storage
# of the message from the original buffer. And also make Torch
# not complain about a readonly memoryview.
buffer = self.aux_buffers[data] if isinstance(data, int) \
else bytearray(data)
return np.ndarray(buffer=buffer, dtype=np.dtype(dtype), shape=shape)
# Create numpy wrapper around the bytes
arr = np.ndarray(buffer=buffer, dtype=np.uint8, shape=(len(buffer), ))
torch_dtype = getattr(torch, dtype)
assert isinstance(torch_dtype, torch.dtype)
# Convert back to proper shape & type
return torch.from_numpy(arr).view(torch_dtype).view(shape)
def _decode_mm_items(self, obj: list) -> list[MultiModalKwargsItem]:
decoded_items = []
@ -228,7 +259,7 @@ class MsgpackDecoder:
if not isinstance(obj, list):
raise TypeError(f"Unexpected NestedTensors contents: {type(obj)}")
if obj and isinstance(obj[0], str):
return torch.from_numpy(self._decode_ndarray(obj))
return self._decode_tensor(obj)
return [self._decode_nested_tensors(x) for x in obj]
def ext_hook(self, code: int, data: memoryview) -> Any: