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from __future__ import annotations
import math
import numpy as np
from distributed.protocol import pickle
from distributed.protocol.serialize import dask_deserialize, dask_serialize
from distributed.utils import log_errors
def itemsize(dt):
"""Itemsize of dtype
Try to return the itemsize of the base element, return 8 as a fallback
"""
result = dt.base.itemsize
if result > 255:
result = 8
return result
@dask_serialize.register(np.ndarray)
def serialize_numpy_ndarray(x, context=None):
if x.dtype.hasobject or (x.dtype.flags & np.core.multiarray.LIST_PICKLE):
header = {"pickle": True}
frames = [None]
def buffer_callback(f):
frames.append(memoryview(f))
frames[0] = pickle.dumps(
x,
buffer_callback=buffer_callback,
protocol=(context or {}).get("pickle-protocol", None),
)
return header, frames
# We cannot blindly pickle the dtype as some may fail pickling,
# so we have a mixture of strategies.
if x.dtype.kind == "V":
# Preserving all the information works best when pickling
try:
# Only use stdlib pickle as cloudpickle is slow when failing
# (microseconds instead of nanoseconds)
dt = (
1,
pickle.pickle.dumps(
x.dtype, protocol=(context or {}).get("pickle-protocol", None)
),
)
pickle.loads(dt[1]) # does it unpickle fine?
except Exception:
# dtype fails pickling => fall back on the descr if reasonable.
if x.dtype.type is not np.void or x.dtype.alignment != 1:
raise
else:
dt = (0, x.dtype.descr)
else:
dt = (0, x.dtype.str)
# Only serialize broadcastable data for arrays with zero strided axes
broadcast_to = None
if 0 in x.strides:
broadcast_to = x.shape
strides = x.strides
writeable = x.flags.writeable
x = x[tuple(slice(None) if s != 0 else slice(1) for s in strides)]
if not x.flags.c_contiguous and not x.flags.f_contiguous:
# Broadcasting can only be done with contiguous arrays
x = np.ascontiguousarray(x)
x = np.lib.stride_tricks.as_strided(
x,
strides=[j if i != 0 else i for i, j in zip(strides, x.strides)],
writeable=writeable,
)
if not x.shape:
# 0d array
strides = x.strides
data = x.ravel()
elif x.flags.c_contiguous or x.flags.f_contiguous:
# Avoid a copy and respect order when unserializing
strides = x.strides
data = x.ravel(order="K")
else:
x = np.ascontiguousarray(x)
strides = x.strides
data = x.ravel()
if data.dtype.fields or data.dtype.itemsize > 8:
data = data.view("u%d" % math.gcd(x.dtype.itemsize, 8))
try:
data = data.data
except ValueError:
# "ValueError: cannot include dtype 'M' in a buffer"
data = data.view("u%d" % math.gcd(x.dtype.itemsize, 8)).data
header = {
"dtype": dt,
"shape": x.shape,
"strides": strides,
"writeable": [x.flags.writeable],
}
if broadcast_to is not None:
header["broadcast_to"] = broadcast_to
frames = [data]
return header, frames
@dask_deserialize.register(np.ndarray)
@log_errors
def deserialize_numpy_ndarray(header, frames):
if header.get("pickle"):
return pickle.loads(frames[0], buffers=frames[1:])
(frame,) = frames
(writeable,) = header["writeable"]
is_custom, dt = header["dtype"]
if is_custom:
dt = pickle.loads(dt)
else:
dt = np.dtype(dt)
if header.get("broadcast_to"):
shape = header["broadcast_to"]
else:
shape = header["shape"]
x = np.ndarray(shape, dtype=dt, buffer=frame, strides=header["strides"])
if not writeable:
x.flags.writeable = False
else:
x = np.require(x, requirements=["W"])
return x
@dask_serialize.register(np.ma.core.MaskedConstant)
def serialize_numpy_ma_masked(x):
return {}, []
@dask_deserialize.register(np.ma.core.MaskedConstant)
def deserialize_numpy_ma_masked(header, frames):
return np.ma.masked
@dask_serialize.register(np.ma.core.MaskedArray)
def serialize_numpy_maskedarray(x, context=None):
data_header, frames = serialize_numpy_ndarray(x.data)
header = {"data-header": data_header, "nframes": len(frames)}
# Serialize mask if present
if x.mask is not np.ma.nomask:
mask_header, mask_frames = serialize_numpy_ndarray(x.mask)
header["mask-header"] = mask_header
frames += mask_frames
# Only a few dtypes have python equivalents msgpack can serialize
if isinstance(x.fill_value, (np.integer, np.floating, np.bool_)):
serialized_fill_value = (False, x.fill_value.item())
else:
serialized_fill_value = (
True,
pickle.dumps(
x.fill_value, protocol=(context or {}).get("pickle-protocol", None)
),
)
header["fill-value"] = serialized_fill_value
return header, frames
@dask_deserialize.register(np.ma.core.MaskedArray)
def deserialize_numpy_maskedarray(header, frames):
data_header = header["data-header"]
data_frames = frames[: header["nframes"]]
data = deserialize_numpy_ndarray(data_header, data_frames)
if "mask-header" in header:
mask_header = header["mask-header"]
mask_frames = frames[header["nframes"] :]
mask = deserialize_numpy_ndarray(mask_header, mask_frames)
else:
mask = np.ma.nomask
pickled_fv, fill_value = header["fill-value"]
if pickled_fv:
fill_value = pickle.loads(fill_value)
return np.ma.masked_array(data, mask=mask, fill_value=fill_value)
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