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1198 lines (1063 loc) · 42.5 KB
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# Data Parallel Control (dpctl)
#
# Copyright 2020-2025 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# distutils: language = c++
# cython: language_level=3
# cython: linetrace=True
cdef extern from "numpy/npy_no_deprecated_api.h":
pass
cimport cpython
from libc cimport stdlib
from libc.stdint cimport int64_t, uint8_t, uint16_t, uint32_t, uint64_t
from numpy cimport ndarray
cimport dpctl as c_dpctl
cimport dpctl.memory as c_dpmem
from dpctl._sycl_queue_manager cimport get_device_cached_queue
from .._backend cimport (
DPCTLDevice_Delete,
DPCTLDevice_GetParentDevice,
DPCTLSyclDeviceRef,
DPCTLSyclUSMRef,
)
from ._usmarray cimport USM_ARRAY_WRITABLE, usm_ndarray
import ctypes
import numpy as np
import dpctl
import dpctl.memory as dpmem
from ._device import Device
cdef extern from "dlpack/dlpack.h" nogil:
cdef int DLPACK_MAJOR_VERSION
cdef int DLPACK_MINOR_VERSION
cdef int DLPACK_FLAG_BITMASK_READ_ONLY
cdef int DLPACK_FLAG_BITMASK_IS_COPIED
ctypedef struct DLPackVersion:
uint32_t major
uint32_t minor
cdef enum DLDeviceType:
kDLCPU
kDLCUDA
kDLCUDAHost
kDLCUDAManaged
kDLROCM
kDLROCMHost
kDLOpenCL
kDLVulkan
kDLMetal
kDLVPI
kDLOneAPI
kDLWebGPU
kDLHexagon
kDLMAIA
kDLTrn
ctypedef struct DLDevice:
DLDeviceType device_type
int device_id
cdef enum DLDataTypeCode:
kDLInt
kDLUInt
kDLFloat
kDLBfloat
kDLComplex
kDLBool
kDLFloat8_e3m4
kDLFloat8_e4m3
kDLFloat8_e4m3b11fnuz
kDLFloat8_e4m3fn
kDLFloat8_e4m3fnuz
kDLFloat8_e5m2
kDLFloat8_e5m2fnuz
kDLFloat8_e8m0fnu
kDLFloat6_e2m3fn
kDLFloat6_e3m2fn
kDLFloat4_e2m1fn
ctypedef struct DLDataType:
uint8_t code
uint8_t bits
uint16_t lanes
ctypedef struct DLTensor:
void *data
DLDevice device
int ndim
DLDataType dtype
int64_t *shape
int64_t *strides
uint64_t byte_offset
ctypedef struct DLManagedTensor:
DLTensor dl_tensor
void *manager_ctx
void (*deleter)(DLManagedTensor *) # noqa: E211
ctypedef struct DLManagedTensorVersioned:
DLPackVersion version
void *manager_ctx
void (*deleter)(DLManagedTensorVersioned *) # noqa: E211
uint64_t flags
DLTensor dl_tensor
def get_build_dlpack_version():
"""
Returns a tuple of integers representing the `major` and `minor`
version of DLPack :module:`dpctl.tensor` was built with.
This tuple can be passed as the `max_version` argument to
`__dlpack__` to guarantee module:`dpctl.tensor` can properly
consume capsule.
Returns:
Tuple[int, int]
A tuple of integers representing the `major` and `minor`
version of DLPack used to build :module:`dpctl.tensor`.
"""
return (DLPACK_MAJOR_VERSION, DLPACK_MINOR_VERSION)
cdef void _pycapsule_deleter(object dlt_capsule) noexcept:
cdef DLManagedTensor *dlm_tensor = NULL
if cpython.PyCapsule_IsValid(dlt_capsule, "dltensor"):
dlm_tensor = <DLManagedTensor*>cpython.PyCapsule_GetPointer(
dlt_capsule, "dltensor")
dlm_tensor.deleter(dlm_tensor)
cdef void _managed_tensor_deleter(
DLManagedTensor *dlm_tensor
) noexcept with gil:
if dlm_tensor is not NULL:
# we only delete shape, because we make single allocation to
# acommodate both shape and strides if strides are needed
stdlib.free(dlm_tensor.dl_tensor.shape)
cpython.Py_DECREF(<object>dlm_tensor.manager_ctx)
dlm_tensor.manager_ctx = NULL
stdlib.free(dlm_tensor)
cdef void _pycapsule_versioned_deleter(object dlt_capsule) noexcept:
cdef DLManagedTensorVersioned *dlmv_tensor = NULL
if cpython.PyCapsule_IsValid(dlt_capsule, "dltensor_versioned"):
dlmv_tensor = <DLManagedTensorVersioned*>cpython.PyCapsule_GetPointer(
dlt_capsule, "dltensor_versioned")
dlmv_tensor.deleter(dlmv_tensor)
cdef void _managed_tensor_versioned_deleter(
DLManagedTensorVersioned *dlmv_tensor
) noexcept with gil:
if dlmv_tensor is not NULL:
# we only delete shape, because we make single allocation to
# acommodate both shape and strides if strides are needed
stdlib.free(dlmv_tensor.dl_tensor.shape)
cpython.Py_DECREF(<object>dlmv_tensor.manager_ctx)
dlmv_tensor.manager_ctx = NULL
stdlib.free(dlmv_tensor)
cdef object _get_default_context(c_dpctl.SyclDevice dev):
try:
default_context = dev.sycl_platform.default_context
except RuntimeError:
# RT does not support default_context
default_context = None
return default_context
cdef int get_array_dlpack_device_id(
usm_ndarray usm_ary
) except -1:
"""Finds ordinal number of the parent of device where array
was allocated.
"""
cdef c_dpctl.SyclQueue ary_sycl_queue
cdef c_dpctl.SyclDevice ary_sycl_device
cdef DPCTLSyclDeviceRef pDRef = NULL
cdef int device_id = -1
ary_sycl_queue = usm_ary.get_sycl_queue()
ary_sycl_device = ary_sycl_queue.get_sycl_device()
default_context = _get_default_context(ary_sycl_device)
if default_context is None:
# check that ary_sycl_device is a non-partitioned device
pDRef = DPCTLDevice_GetParentDevice(ary_sycl_device.get_device_ref())
if pDRef is not NULL:
DPCTLDevice_Delete(pDRef)
raise DLPackCreationError(
"to_dlpack_capsule: DLPack can only export arrays allocated "
"on non-partitioned SYCL devices on platforms where "
"default_context oneAPI extension is not supported."
)
else:
if not usm_ary.sycl_context == default_context:
raise DLPackCreationError(
"to_dlpack_capsule: DLPack can only export arrays based on USM "
"allocations bound to a default platform SYCL context"
)
device_id = ary_sycl_device.get_device_id()
if device_id < 0:
raise DLPackCreationError(
"get_array_dlpack_device_id: failed to determine device_id"
)
return device_id
cpdef to_dlpack_capsule(usm_ndarray usm_ary):
"""
to_dlpack_capsule(usm_ary)
Constructs named Python capsule object referencing
instance of ``DLManagedTensor`` from
:class:`dpctl.tensor.usm_ndarray` instance.
Args:
usm_ary: An instance of :class:`dpctl.tensor.usm_ndarray`
Returns:
A new capsule with name ``"dltensor"`` that contains
a pointer to ``DLManagedTensor`` struct.
Raises:
DLPackCreationError: when array can be represented as
DLPack tensor. This may happen when array was allocated
on a partitioned sycl device, or its USM allocation is
not bound to the platform default SYCL context.
MemoryError: when host allocation to needed for ``DLManagedTensor``
did not succeed.
ValueError: when array elements data type could not be represented
in ``DLManagedTensor``.
"""
cdef DLManagedTensor *dlm_tensor = NULL
cdef DLTensor *dl_tensor = NULL
cdef int nd = usm_ary.get_ndim()
cdef char *data_ptr = usm_ary.get_data()
cdef Py_ssize_t *shape_ptr = NULL
cdef Py_ssize_t *strides_ptr = NULL
cdef int64_t *shape_strides_ptr = NULL
cdef int i = 0
cdef int device_id = -1
cdef Py_ssize_t element_offset = 0
cdef Py_ssize_t byte_offset = 0
cdef Py_ssize_t si = 1
ary_base = usm_ary.get_base()
device_id = get_array_dlpack_device_id(usm_ary)
dlm_tensor = <DLManagedTensor *> stdlib.malloc(
sizeof(DLManagedTensor))
if dlm_tensor is NULL:
raise MemoryError(
"to_dlpack_capsule: Could not allocate memory for DLManagedTensor"
)
if nd > 0:
shape_strides_ptr = <int64_t *>stdlib.malloc((sizeof(int64_t) * 2) * nd)
if shape_strides_ptr is NULL:
stdlib.free(dlm_tensor)
raise MemoryError(
"to_dlpack_capsule: Could not allocate memory for shape/strides"
)
shape_ptr = usm_ary.get_shape()
for i in range(nd):
shape_strides_ptr[i] = shape_ptr[i]
strides_ptr = usm_ary.get_strides()
if strides_ptr:
for i in range(nd):
shape_strides_ptr[nd + i] = strides_ptr[i]
else:
si = 1
for i in range(0, nd):
shape_strides_ptr[nd + i] = si
si = si * shape_ptr[i]
strides_ptr = <Py_ssize_t *>&shape_strides_ptr[nd]
ary_dt = usm_ary.dtype
ary_dtk = ary_dt.kind
element_offset = usm_ary.get_offset()
byte_offset = element_offset * (<Py_ssize_t>ary_dt.itemsize)
dl_tensor = &dlm_tensor.dl_tensor
dl_tensor.data = <void*>(data_ptr - byte_offset)
dl_tensor.ndim = nd
dl_tensor.byte_offset = <uint64_t>byte_offset
dl_tensor.shape = &shape_strides_ptr[0] if nd > 0 else NULL
dl_tensor.strides = &shape_strides_ptr[nd] if nd > 0 else NULL
dl_tensor.device.device_type = kDLOneAPI
dl_tensor.device.device_id = device_id
dl_tensor.dtype.lanes = <uint16_t>1
dl_tensor.dtype.bits = <uint8_t>(ary_dt.itemsize * 8)
if (ary_dtk == "b"):
dl_tensor.dtype.code = <uint8_t>kDLBool
elif (ary_dtk == "u"):
dl_tensor.dtype.code = <uint8_t>kDLUInt
elif (ary_dtk == "i"):
dl_tensor.dtype.code = <uint8_t>kDLInt
elif (ary_dtk == "f"):
dl_tensor.dtype.code = <uint8_t>kDLFloat
elif (ary_dtk == "c"):
dl_tensor.dtype.code = <uint8_t>kDLComplex
else:
stdlib.free(shape_strides_ptr)
stdlib.free(dlm_tensor)
raise ValueError("Unrecognized array data type")
dlm_tensor.manager_ctx = <void*>ary_base
cpython.Py_INCREF(ary_base)
dlm_tensor.deleter = _managed_tensor_deleter
return cpython.PyCapsule_New(dlm_tensor, "dltensor", _pycapsule_deleter)
cpdef to_dlpack_versioned_capsule(usm_ndarray usm_ary, bint copied):
"""
to_dlpack_versioned_capsule(usm_ary, copied)
Constructs named Python capsule object referencing
instance of ``DLManagedTensorVersioned`` from
:class:`dpctl.tensor.usm_ndarray` instance.
Args:
usm_ary: An instance of :class:`dpctl.tensor.usm_ndarray`
copied: A bint representing whether the data was previously
copied in order to set the flags with the is-copied
bitmask.
Returns:
A new capsule with name ``"dltensor_versioned"`` that
contains a pointer to ``DLManagedTensorVersioned`` struct.
Raises:
DLPackCreationError: when array can be represented as
DLPack tensor. This may happen when array was allocated
on a partitioned sycl device, or its USM allocation is
not bound to the platform default SYCL context.
MemoryError: when host allocation to needed for
``DLManagedTensorVersioned`` did not succeed.
ValueError: when array elements data type could not be represented
in ``DLManagedTensorVersioned``.
"""
cdef DLManagedTensorVersioned *dlmv_tensor = NULL
cdef DLTensor *dl_tensor = NULL
cdef uint32_t dlmv_flags = 0
cdef int nd = usm_ary.get_ndim()
cdef char *data_ptr = usm_ary.get_data()
cdef Py_ssize_t *shape_ptr = NULL
cdef Py_ssize_t *strides_ptr = NULL
cdef int64_t *shape_strides_ptr = NULL
cdef int i = 0
cdef int device_id = -1
cdef int flags = 0
cdef Py_ssize_t element_offset = 0
cdef Py_ssize_t byte_offset = 0
cdef Py_ssize_t si = 1
ary_base = usm_ary.get_base()
# Find ordinal number of the parent device
device_id = get_array_dlpack_device_id(usm_ary)
dlmv_tensor = <DLManagedTensorVersioned *> stdlib.malloc(
sizeof(DLManagedTensorVersioned))
if dlmv_tensor is NULL:
raise MemoryError(
"to_dlpack_versioned_capsule: Could not allocate memory "
"for DLManagedTensorVersioned"
)
if nd > 0:
shape_strides_ptr = <int64_t *>stdlib.malloc((sizeof(int64_t) * 2) * nd)
if shape_strides_ptr is NULL:
stdlib.free(dlmv_tensor)
raise MemoryError(
"to_dlpack_versioned_capsule: Could not allocate memory "
"for shape/strides"
)
# this can be a separate function for handling shapes and strides
shape_ptr = usm_ary.get_shape()
for i in range(nd):
shape_strides_ptr[i] = shape_ptr[i]
strides_ptr = usm_ary.get_strides()
flags = usm_ary.flags_
if strides_ptr:
for i in range(nd):
shape_strides_ptr[nd + i] = strides_ptr[i]
else:
si = 1
for i in range(0, nd):
shape_strides_ptr[nd + i] = si
si = si * shape_ptr[i]
strides_ptr = <Py_ssize_t *>&shape_strides_ptr[nd]
# this can all be a function for building the dl_tensor
# object (separate from dlm/dlmv)
ary_dt = usm_ary.dtype
ary_dtk = ary_dt.kind
element_offset = usm_ary.get_offset()
byte_offset = element_offset * (<Py_ssize_t>ary_dt.itemsize)
dl_tensor = &dlmv_tensor.dl_tensor
dl_tensor.data = <void*>(data_ptr - byte_offset)
dl_tensor.ndim = nd
dl_tensor.byte_offset = <uint64_t>byte_offset
dl_tensor.shape = &shape_strides_ptr[0] if nd > 0 else NULL
dl_tensor.strides = &shape_strides_ptr[nd] if nd > 0 else NULL
dl_tensor.device.device_type = kDLOneAPI
dl_tensor.device.device_id = device_id
dl_tensor.dtype.lanes = <uint16_t>1
dl_tensor.dtype.bits = <uint8_t>(ary_dt.itemsize * 8)
if (ary_dtk == "b"):
dl_tensor.dtype.code = <uint8_t>kDLBool
elif (ary_dtk == "u"):
dl_tensor.dtype.code = <uint8_t>kDLUInt
elif (ary_dtk == "i"):
dl_tensor.dtype.code = <uint8_t>kDLInt
elif (ary_dtk == "f"):
dl_tensor.dtype.code = <uint8_t>kDLFloat
elif (ary_dtk == "c"):
dl_tensor.dtype.code = <uint8_t>kDLComplex
else:
stdlib.free(shape_strides_ptr)
stdlib.free(dlmv_tensor)
raise ValueError("Unrecognized array data type")
# set flags down here
if copied:
dlmv_flags |= DLPACK_FLAG_BITMASK_IS_COPIED
if not (flags & USM_ARRAY_WRITABLE):
dlmv_flags |= DLPACK_FLAG_BITMASK_READ_ONLY
dlmv_tensor.flags = dlmv_flags
dlmv_tensor.version.major = DLPACK_MAJOR_VERSION
dlmv_tensor.version.minor = DLPACK_MINOR_VERSION
dlmv_tensor.manager_ctx = <void*>ary_base
cpython.Py_INCREF(ary_base)
dlmv_tensor.deleter = _managed_tensor_versioned_deleter
return cpython.PyCapsule_New(
dlmv_tensor, "dltensor_versioned", _pycapsule_versioned_deleter
)
cpdef numpy_to_dlpack_versioned_capsule(ndarray npy_ary, bint copied):
"""
to_dlpack_versioned_capsule(npy_ary, copied)
Constructs named Python capsule object referencing
instance of ``DLManagedTensorVersioned`` from
:class:`numpy.ndarray` instance.
Args:
npy_ary: An instance of :class:`numpy.ndarray`
copied: A bint representing whether the data was previously
copied in order to set the flags with the is-copied
bitmask.
Returns:
A new capsule with name ``"dltensor_versioned"`` that
contains a pointer to ``DLManagedTensorVersioned`` struct.
Raises:
DLPackCreationError: when array can be represented as
DLPack tensor.
MemoryError: when host allocation to needed for
``DLManagedTensorVersioned`` did not succeed.
ValueError: when array elements data type could not be represented
in ``DLManagedTensorVersioned``.
"""
cdef DLManagedTensorVersioned *dlmv_tensor = NULL
cdef DLTensor *dl_tensor = NULL
cdef uint32_t dlmv_flags = 0
cdef int nd = npy_ary.ndim
cdef int64_t *shape_strides_ptr = NULL
cdef int i = 0
cdef Py_ssize_t byte_offset = 0
cdef int itemsize = npy_ary.itemsize
dlmv_tensor = <DLManagedTensorVersioned *> stdlib.malloc(
sizeof(DLManagedTensorVersioned))
if dlmv_tensor is NULL:
raise MemoryError(
"numpy_to_dlpack_versioned_capsule: Could not allocate memory "
"for DLManagedTensorVersioned"
)
shape = npy_ary.ctypes.shape_as(ctypes.c_int64)
strides = npy_ary.ctypes.strides_as(ctypes.c_int64)
if nd > 0:
if npy_ary.size != 1:
for i in range(nd):
if shape[i] != 1 and strides[i] % itemsize != 0:
stdlib.free(dlmv_tensor)
raise BufferError(
"numpy_to_dlpack_versioned_capsule: DLPack cannot "
"encode an array if strides are not a multiple of "
"itemsize"
)
shape_strides_ptr = <int64_t *>stdlib.malloc((sizeof(int64_t) * 2) * nd)
if shape_strides_ptr is NULL:
stdlib.free(dlmv_tensor)
raise MemoryError(
"numpy_to_dlpack_versioned_capsule: Could not allocate memory "
"for shape/strides"
)
for i in range(nd):
shape_strides_ptr[i] = shape[i]
shape_strides_ptr[nd + i] = strides[i] // itemsize
writable_flag = npy_ary.flags["W"]
ary_dt = npy_ary.dtype
ary_dtk = ary_dt.kind
dl_tensor = &dlmv_tensor.dl_tensor
dl_tensor.data = <void *> npy_ary.data
dl_tensor.ndim = nd
dl_tensor.byte_offset = <uint64_t>byte_offset
dl_tensor.shape = &shape_strides_ptr[0] if nd > 0 else NULL
dl_tensor.strides = &shape_strides_ptr[nd] if nd > 0 else NULL
dl_tensor.device.device_type = kDLCPU
dl_tensor.device.device_id = 0
dl_tensor.dtype.lanes = <uint16_t>1
dl_tensor.dtype.bits = <uint8_t>(ary_dt.itemsize * 8)
if (ary_dtk == "b"):
dl_tensor.dtype.code = <uint8_t>kDLBool
elif (ary_dtk == "u"):
dl_tensor.dtype.code = <uint8_t>kDLUInt
elif (ary_dtk == "i"):
dl_tensor.dtype.code = <uint8_t>kDLInt
elif (ary_dtk == "f" and ary_dt.itemsize <= 8):
dl_tensor.dtype.code = <uint8_t>kDLFloat
elif (ary_dtk == "c" and ary_dt.itemsize <= 16):
dl_tensor.dtype.code = <uint8_t>kDLComplex
else:
stdlib.free(shape_strides_ptr)
stdlib.free(dlmv_tensor)
raise ValueError("Unrecognized array data type")
# set flags down here
if copied:
dlmv_flags |= DLPACK_FLAG_BITMASK_IS_COPIED
if not writable_flag:
dlmv_flags |= DLPACK_FLAG_BITMASK_READ_ONLY
dlmv_tensor.flags = dlmv_flags
dlmv_tensor.version.major = DLPACK_MAJOR_VERSION
dlmv_tensor.version.minor = DLPACK_MINOR_VERSION
dlmv_tensor.manager_ctx = <void*>npy_ary
cpython.Py_INCREF(npy_ary)
dlmv_tensor.deleter = _managed_tensor_versioned_deleter
return cpython.PyCapsule_New(
dlmv_tensor, "dltensor_versioned", _pycapsule_versioned_deleter
)
cdef class _DLManagedTensorOwner:
"""
Helper class managing the lifetime of the DLManagedTensor struct
transferred from a 'dlpack' capsule.
"""
cdef DLManagedTensor * dlm_tensor
def __cinit__(self):
self.dlm_tensor = NULL
def __dealloc__(self):
if self.dlm_tensor:
self.dlm_tensor.deleter(self.dlm_tensor)
self.dlm_tensor = NULL
@staticmethod
cdef _DLManagedTensorOwner _create(DLManagedTensor *dlm_tensor_src):
cdef _DLManagedTensorOwner res
res = _DLManagedTensorOwner.__new__(_DLManagedTensorOwner)
res.dlm_tensor = dlm_tensor_src
return res
cdef class _DLManagedTensorVersionedOwner:
"""
Helper class managing the lifetime of the DLManagedTensorVersioned
struct transferred from a 'dlpack_versioned' capsule.
"""
cdef DLManagedTensorVersioned * dlmv_tensor
def __cinit__(self):
self.dlmv_tensor = NULL
def __dealloc__(self):
if self.dlmv_tensor:
self.dlmv_tensor.deleter(self.dlmv_tensor)
self.dlmv_tensor = NULL
@staticmethod
cdef _DLManagedTensorVersionedOwner _create(
DLManagedTensorVersioned *dlmv_tensor_src
):
cdef _DLManagedTensorVersionedOwner res
res = _DLManagedTensorVersionedOwner.__new__(
_DLManagedTensorVersionedOwner
)
res.dlmv_tensor = dlmv_tensor_src
return res
cdef dict _numpy_array_interface_from_dl_tensor(DLTensor *dlt, bint ro_flag):
"""Constructs a NumPy `__array_interface__` dictionary from a DLTensor."""
cdef int itemsize = 0
if dlt.dtype.lanes != 1:
raise BufferError(
"Can not import DLPack tensor with lanes != 1"
)
itemsize = dlt.dtype.bits // 8
shape = list()
if (dlt.strides is NULL):
strides = None
for dim in range(dlt.ndim):
shape.append(dlt.shape[dim])
else:
strides = list()
for dim in range(dlt.ndim):
shape.append(dlt.shape[dim])
# convert to byte-strides
strides.append(dlt.strides[dim] * itemsize)
strides = tuple(strides)
shape = tuple(shape)
if (dlt.dtype.code == kDLUInt):
ary_dt = "u" + str(itemsize)
elif (dlt.dtype.code == kDLInt):
ary_dt = "i" + str(itemsize)
elif (dlt.dtype.code == kDLFloat):
ary_dt = "f" + str(itemsize)
elif (dlt.dtype.code == kDLComplex):
ary_dt = "c" + str(itemsize)
elif (dlt.dtype.code == kDLBool):
ary_dt = "b" + str(itemsize)
else:
raise BufferError(
"Can not import DLPack tensor with type code {}.".format(
<object>dlt.dtype.code
)
)
typestr = "|" + ary_dt
return dict(
version=3,
shape=shape,
strides=strides,
data=(<size_t> dlt.data, True if ro_flag else False),
offset=dlt.byte_offset,
typestr=typestr,
)
class _numpy_array_interface_wrapper:
"""
Class that wraps a Python capsule and dictionary for consumption by NumPy.
Implementation taken from
https://github.com/dmlc/dlpack/blob/main/apps/numpy_dlpack/dlpack/to_numpy.py
Args:
array_interface:
A dictionary describing the underlying memory. Formatted
to match `numpy.ndarray.__array_interface__`.
pycapsule:
A Python capsule wrapping the dlpack tensor that will be
converted to numpy.
"""
def __init__(self, array_interface, memory_owner) -> None:
self.__array_interface__ = array_interface
self._memory_owner = memory_owner
cdef bint _is_kdlcpu_device(DLDevice *dev):
"Check if DLTensor.DLDevice denotes (kDLCPU, 0)"
return (dev[0].device_type == kDLCPU) and (dev[0].device_id == 0)
cpdef object from_dlpack_capsule(object py_caps):
"""
from_dlpack_capsule(py_caps)
Reconstructs instance of :class:`dpctl.tensor.usm_ndarray` from
named Python capsule object referencing instance of ``DLManagedTensor``
without copy. The instance forms a view in the memory of the tensor.
Args:
caps:
Python capsule with name ``"dltensor"`` expected to reference
an instance of ``DLManagedTensor`` struct.
Returns:
Instance of :class:`dpctl.tensor.usm_ndarray` with a view into
memory of the tensor. Capsule is renamed to ``"used_dltensor"``
upon success.
Raises:
TypeError:
if argument is not a ``"dltensor"`` capsule.
ValueError:
if argument is ``"used_dltensor"`` capsule
BufferError:
if the USM pointer is not bound to the reconstructed
sycl context, or the DLPack's device_type is not supported
by :mod:`dpctl`.
"""
cdef DLManagedTensorVersioned *dlmv_tensor = NULL
cdef DLManagedTensor *dlm_tensor = NULL
cdef DLTensor *dl_tensor = NULL
cdef int versioned = 0
cdef int readonly = 0
cdef bytes usm_type
cdef size_t sz = 1
cdef size_t alloc_sz = 1
cdef int i
cdef int device_id = -1
cdef int element_bytesize = 0
cdef Py_ssize_t offset_min = 0
cdef Py_ssize_t offset_max = 0
cdef char *mem_ptr = NULL
cdef Py_ssize_t mem_ptr_delta = 0
cdef Py_ssize_t element_offset = 0
cdef int64_t stride_i = -1
cdef int64_t shape_i = -1
if cpython.PyCapsule_IsValid(py_caps, "dltensor"):
dlm_tensor = <DLManagedTensor*>cpython.PyCapsule_GetPointer(
py_caps, "dltensor")
dl_tensor = &dlm_tensor.dl_tensor
elif cpython.PyCapsule_IsValid(py_caps, "dltensor_versioned"):
dlmv_tensor = <DLManagedTensorVersioned*>cpython.PyCapsule_GetPointer(
py_caps, "dltensor_versioned")
if dlmv_tensor.version.major > DLPACK_MAJOR_VERSION:
raise BufferError(
"Can not import DLPack tensor with major version "
f"greater than {DLPACK_MAJOR_VERSION}"
)
versioned = 1
readonly = (dlmv_tensor.flags & DLPACK_FLAG_BITMASK_READ_ONLY) != 0
dl_tensor = &dlmv_tensor.dl_tensor
elif (
cpython.PyCapsule_IsValid(py_caps, "used_dltensor")
or cpython.PyCapsule_IsValid(py_caps, "used_dltensor_versioned")
):
raise ValueError(
"A DLPack tensor object can not be consumed multiple times"
)
else:
raise TypeError(
"`from_dlpack_capsule` expects a Python 'dltensor' capsule"
)
# Verify that we can work with this device
if dl_tensor.device.device_type == kDLOneAPI:
device_id = dl_tensor.device.device_id
root_device = dpctl.SyclDevice(str(<int>device_id))
try:
default_context = root_device.sycl_platform.default_context
except RuntimeError:
default_context = get_device_cached_queue(root_device).sycl_context
if dl_tensor.data is NULL:
usm_type = b"device"
q = get_device_cached_queue((default_context, root_device,))
else:
usm_type = c_dpmem._Memory.get_pointer_type(
<DPCTLSyclUSMRef> dl_tensor.data,
<c_dpctl.SyclContext>default_context)
if usm_type == b"unknown":
raise BufferError(
"Data pointer in DLPack is not bound to default sycl "
f"context of device '{device_id}', translated to "
f"{root_device.filter_string}"
)
alloc_device = c_dpmem._Memory.get_pointer_device(
<DPCTLSyclUSMRef> dl_tensor.data,
<c_dpctl.SyclContext>default_context
)
q = get_device_cached_queue((default_context, alloc_device,))
if dl_tensor.dtype.bits % 8:
raise BufferError(
"Can not import DLPack tensor whose element's "
"bitsize is not a multiple of 8"
)
if dl_tensor.dtype.lanes != 1:
raise BufferError(
"Can not import DLPack tensor with lanes != 1"
)
if dl_tensor.ndim > 0:
offset_min = 0
offset_max = 0
for i in range(dl_tensor.ndim):
stride_i = dl_tensor.strides[i]
shape_i = dl_tensor.shape[i]
if shape_i > 1:
shape_i -= 1
if stride_i > 0:
offset_max = offset_max + stride_i * shape_i
else:
offset_min = offset_min + stride_i * shape_i
sz = offset_max - offset_min + 1
if sz == 0:
sz = 1
element_bytesize = (dl_tensor.dtype.bits // 8)
sz = sz * element_bytesize
element_offset = dl_tensor.byte_offset // element_bytesize
# transfer ownership
if not versioned:
dlm_holder = _DLManagedTensorOwner._create(dlm_tensor)
cpython.PyCapsule_SetName(py_caps, "used_dltensor")
else:
dlmv_holder = _DLManagedTensorVersionedOwner._create(dlmv_tensor)
cpython.PyCapsule_SetName(py_caps, "used_dltensor_versioned")
if dl_tensor.data is NULL:
usm_mem = dpmem.MemoryUSMDevice(sz, q)
else:
mem_ptr_delta = dl_tensor.byte_offset - (
element_offset * element_bytesize
)
mem_ptr = <char *>dl_tensor.data
alloc_sz = dl_tensor.byte_offset + <uint64_t>(
(offset_max + 1) * element_bytesize)
tmp = c_dpmem._Memory.create_from_usm_pointer_size_qref(
<DPCTLSyclUSMRef> mem_ptr,
max(alloc_sz, <uint64_t>element_bytesize),
(<c_dpctl.SyclQueue>q).get_queue_ref(),
memory_owner=dlmv_holder if versioned else dlm_holder
)
if mem_ptr_delta == 0:
usm_mem = tmp
else:
alloc_sz = dl_tensor.byte_offset + <uint64_t>(
(offset_max * element_bytesize + mem_ptr_delta))
usm_mem = c_dpmem._Memory.create_from_usm_pointer_size_qref(
<DPCTLSyclUSMRef> (
mem_ptr + (element_bytesize - mem_ptr_delta)
),
max(alloc_sz, <uint64_t>element_bytesize),
(<c_dpctl.SyclQueue>q).get_queue_ref(),
memory_owner=tmp
)
py_shape = list()
if (dl_tensor.shape is not NULL):
for i in range(dl_tensor.ndim):
py_shape.append(dl_tensor.shape[i])
if (dl_tensor.strides is not NULL):
py_strides = list()
for i in range(dl_tensor.ndim):
py_strides.append(dl_tensor.strides[i])
else:
py_strides = None
if (dl_tensor.dtype.code == kDLUInt):
ary_dt = np.dtype("u" + str(element_bytesize))
elif (dl_tensor.dtype.code == kDLInt):
ary_dt = np.dtype("i" + str(element_bytesize))
elif (dl_tensor.dtype.code == kDLFloat):
ary_dt = np.dtype("f" + str(element_bytesize))
elif (dl_tensor.dtype.code == kDLComplex):
ary_dt = np.dtype("c" + str(element_bytesize))
elif (dl_tensor.dtype.code == kDLBool):
ary_dt = np.dtype("?")
else:
raise BufferError(
"Can not import DLPack tensor with type code {}.".format(
<object>dl_tensor.dtype.code
)
)
res_ary = usm_ndarray(
py_shape,
dtype=ary_dt,
buffer=usm_mem,
strides=py_strides,
offset=element_offset
)
if readonly:
res_ary.flags_ = (res_ary.flags_ & ~USM_ARRAY_WRITABLE)
return res_ary
elif _is_kdlcpu_device(&dl_tensor.device):
ary_iface = _numpy_array_interface_from_dl_tensor(dl_tensor, readonly)
if not versioned:
dlm_holder = _DLManagedTensorOwner._create(dlm_tensor)
cpython.PyCapsule_SetName(py_caps, "used_dltensor")
return np.ctypeslib.as_array(
_numpy_array_interface_wrapper(ary_iface, dlm_holder)
)
else:
dlmv_holder = _DLManagedTensorVersionedOwner._create(dlmv_tensor)
cpython.PyCapsule_SetName(py_caps, "used_dltensor_versioned")
return np.ctypeslib.as_array(
_numpy_array_interface_wrapper(ary_iface, dlmv_holder)
)
else:
raise BufferError(
"The DLPack tensor resides on unsupported device."
)
cdef usm_ndarray _to_usm_ary_from_host_blob(object host_blob, dev : Device):
q = dev.sycl_queue
np_ary = np.asarray(host_blob)
dt = np_ary.dtype
if dt.char in "dD" and q.sycl_device.has_aspect_fp64 is False:
Xusm_dtype = (
"float32" if dt.char == "d" else "complex64"
)
else:
Xusm_dtype = dt
usm_mem = dpmem.MemoryUSMDevice(np_ary.nbytes, queue=q)
usm_ary = usm_ndarray(np_ary.shape, dtype=Xusm_dtype, buffer=usm_mem)
usm_mem.copy_from_host(np.reshape(np_ary.view(dtype="u1"), -1))
return usm_ary
# only cdef to make it private
cdef object _create_device(object device, object dl_device):
if isinstance(device, Device):
return device
elif isinstance(device, dpctl.SyclDevice):
return Device.create_device(device)
else:
root_device = dpctl.SyclDevice(str(<int>dl_device[1]))
return Device.create_device(root_device)
def from_dlpack(x, /, *, device=None, copy=None):
"""from_dlpack(x, /, *, device=None, copy=None)
Constructs :class:`dpctl.tensor.usm_ndarray` or :class:`numpy.ndarray`
instance from a Python object ``x`` that implements ``__dlpack__`` protocol.
Args:
x (object):
A Python object representing an array that supports
``__dlpack__`` protocol.
device (
Optional[str, :class:`dpctl.SyclDevice`,
:class:`dpctl.SyclQueue`,
:class:`dpctl.tensor.Device`,
tuple([:class:`enum.IntEnum`, int])])
):
Device where the output array is to be placed. ``device`` keyword
values can be:
* ``None``
The data remains on the same device.
* oneAPI filter selector string
SYCL device selected by :ref:`filter selector string
<filter_selector_string>`.
* :class:`dpctl.SyclDevice`
explicit SYCL device that must correspond to
a non-partitioned SYCL device.
* :class:`dpctl.SyclQueue`
implies SYCL device targeted by the SYCL queue.
* :class:`dpctl.tensor.Device`
implies SYCL device `device.sycl_queue`. The `Device` object
is obtained via :attr:`dpctl.tensor.usm_ndarray.device`.
* ``(device_type, device_id)``
2-tuple matching the format of the output of the
``__dlpack_device__`` method: an integer enumerator representing
the device type followed by an integer representing the index of
the device. The only supported :class:`dpctl.tensor.DLDeviceType`
device types are ``"kDLCPU"`` and ``"kDLOneAPI"``.
Default: ``None``.
copy (bool, optional)
Boolean indicating whether or not to copy the input.
* If ``copy`` is ``True``, the input will always be
copied.
* If ``False``, a ``BufferError`` will be raised if a