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Initial support for Numpy subclasses (for dparray) #116

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Initial support for Numpy subclasses (for dparray) #116

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ee5e88b
applied patch to allow subclass of ndarray to be maintained and to ha…
DrTodd13 Nov 11, 2020
56d5bc7
dparray changes
DrTodd13 Nov 12, 2020
ca10de8
Code review changes.
DrTodd13 Nov 18, 2020
7ec48a2
Move to numba-dppy
PokhodenkoSA Dec 1, 2020
7fcf540
Move to dpctl
PokhodenkoSA Dec 1, 2020
0b15c43
Merge pull request #1 from PokhodenkoSA/clean-merge_dparray
DrTodd13 Dec 2, 2020
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Code review changes.
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@DrTodd13
DrTodd13 committed Nov 18, 2020
commit ca10de8dd1df56e28ac4b600b9c8bc1a59e49ff6
128 changes: 10 additions & 118 deletions numba/dppl/dparray.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
#from ._ndarray_utils import _transmogrify
import numpy as np
from inspect import getmembers, isfunction, isclass, isbuiltin
from numbers import Number
Expand All @@ -11,7 +10,7 @@
from llvmlite import ir
import llvmlite.llvmpy.core as lc
import llvmlite.binding as llb
from numba.core import types, cgutils
from numba.core import types, cgutils, config
import builtins
import sys
from ctypes.util import find_library
Expand All @@ -24,19 +23,17 @@
from numba.core.typing.templates import CallableTemplate
from numba.np.arrayobj import _array_copy

debug = False
debug = config.DEBUG

def dprint(*args):
if debug:
print(*args)
sys.stdout.flush()

flib = find_library('mkl_intel_ilp64')
dprint("flib:", flib)
llb.load_library_permanently(flib)

# This code makes it so that Numba can contain calls into the DPPLSyclInterface library.
sycl_mem_lib = find_library('DPPLSyclInterface')
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dprint("sycl_mem_lib:", sycl_mem_lib)
# Load the symbols from the DPPL Sycl library.
llb.load_library_permanently(sycl_mem_lib)

import dpctl
Expand Down Expand Up @@ -76,7 +73,7 @@ def __new__(subtype, shape,
dprint("buffer None new_obj already has sycl_usm")
else:
dprint("buffer None new_obj will add sycl_usm")
new_obj.__sycl_usm_array_interface__ = {}
setattr(new_obj, array_interface_property, {})
return new_obj
# zero copy if buffer is a usm backed array-like thing
elif hasattr(buffer, array_interface_property):
Expand All @@ -90,7 +87,7 @@ def __new__(subtype, shape,
dprint("buffer None new_obj already has sycl_usm")
else:
dprint("buffer None new_obj will add sycl_usm")
new_obj.__sycl_usm_array_interface__ = {}
setattr(new_obj, array_interface_property, {})
return new_obj
else:
dprint("dparray::ndarray __new__ buffer not None and not sycl_usm")
Expand All @@ -110,13 +107,11 @@ def __new__(subtype, shape,
dprint("buffer None new_obj already has sycl_usm")
else:
dprint("buffer None new_obj will add sycl_usm")
new_obj.__sycl_usm_array_interface__ = {}
setattr(new_obj, array_interface_property, {})
return new_obj

def __array_finalize__(self, obj):
dprint("__array_finalize__:", obj, hex(id(obj)), type(obj))
# import pdb
# pdb.set_trace()
# When called from the explicit constructor, obj is None
if obj is None: return
# When called in new-from-template, `obj` is another instance of our own
Expand Down Expand Up @@ -150,29 +145,8 @@ def __array_finalize__(self, obj):

# Just raise an exception since __array_ufunc__ makes all reasonable cases not
# need the code below.
raise ValueError("Non-MKL allocated ndarray can not viewed as MKL-allocated one without a copy")
raise ValueError("Non-USM allocated ndarray can not viewed as a USM-allocated one without a copy")

"""
# since dparray must have mkl_memory underlying it, a copy must be made
newbuf = dpctl.Memory(nbytes=self.data.nbytes)
new_arr = np.ndarray.__new__(
type(self),
self.shape,
buffer=newbuf, offset=0,
dtype=self.dtype,
strides=self.strides)
np.copyto(new_arr, self)
# We need to modify self to now be mkl_memory-backed ndarray
# We only need to change data and base, but these are not writeable.
#
# Modification can not be done by simply setting self either,
# as self is just a local copy of the instance.
#
# raise ValueError("Non-MKL allocated ndarray can not viewed as MKL-allocated one without a copy")
# Will probably have to raise an exception soon as Numpy may disallow this.
_transmogrify(self, new_arr)
"""

# Tell Numba to not treat this type just like a NumPy ndarray but to propagate its type.
# This way it will use the custom dparray allocator.
__numba_no_subtype_ndarray__ = True
Expand Down Expand Up @@ -236,7 +210,7 @@ def isdef(x):
try:
eval(x)
return True
except NameEror:
except NameError:
return False

for c in class_list:
Expand All @@ -245,24 +219,11 @@ def isdef(x):
continue
# For now we do the simple thing and copy the types from NumPy module into dparray module.
new_func = "%s = np.%s" % (cname, cname)
# new_func = "class %s(np.%s):\n" % (cname, cname)
if cname == "ndarray":
# Implemented explicitly above.
continue
else:
# This is temporary.
# new_func += " pass\n"
# The code below should eventually be made to work and used.
# new_func += " @classmethod\n"
# new_func += " def cast(cls, some_np_obj):\n"
# new_func += " some_np_obj.__class__ = cls\n"
# new_func += " return some_np_obj\n"
pass
try:
the_code = compile(new_func, '__init__', 'exec')
exec(the_code)
except:
print("Failed to exec class", cname)
print("Failed to exec type propagation", cname)
pass

# Redefine all Numpy functions in this module and if they
Expand All @@ -272,7 +233,6 @@ def isdef(x):
for fname in functions_list:
if isdef(fname):
continue
# print("Adding function", fname)
new_func = "def %s(*args, **kwargs):\n" % fname
new_func += " ret = np.%s(*args, **kwargs)\n" % fname
new_func += " if type(ret) == np.ndarray:\n"
Expand Down Expand Up @@ -320,56 +280,6 @@ def typeof_ta_ndarray(val, c):
# object of type DPArray.
register_model(DPArrayType)(numba.core.datamodel.models.ArrayModel)

"""
# This code should not work because you can't pass arbitrary buffer to dparray constructor.

# This tells Numba how to type calls to a DPArray constructor.
@type_callable(ndarray)
def type_ndarray(context):
def typer(shape, ndim, buf):
return DPArrayType(buf.dtype, buf.ndim, buf.layout)
return typer

@overload(ndarray)
def overload_ndarray_constructor(shape, dtype, buf):
print("overload_ndarray_constructor:", shape, dtype, buf)

def ndarray_impl(shape, dtype, buf):
pass

return ndarray_impl

# This tells Numba how to implement calls to a DPArray constructor.
@lower_builtin(ndarray, types.UniTuple, types.DType, types.Array)
def impl_ndarray(context, builder, sig, args):
# Need to allocate and copy here!
shape, ndim, buf = args
return buf

context.nrt._require_nrt()

mod = builder.module
u32 = ir.IntType(32)

# Get the Numba external allocator for USM memory.
ext_allocator_fnty = ir.FunctionType(cgutils.voidptr_t, [])
ext_allocator_fn = mod.get_or_insert_function(ext_allocator_fnty,
name="dparray_get_ext_allocator")
ext_allocator = builder.call(ext_allocator_fn, [])
# Get the Numba function to allocate an aligned array with an external allocator.
fnty = ir.FunctionType(cgutils.voidptr_t, [cgutils.intp_t, u32, cgutils.voidptr_t])
fn = mod.get_or_insert_function(fnty,
name="NRT_MemInfo_alloc_safe_aligned_external")
fn.return_value.add_attribute("noalias")
if isinstance(align, builtins.int):
align = context.get_constant(types.uint32, align)
else:
assert align.type == u32, "align must be a uint32"
newary = builder.call(fn, [size, align, ext_allocator])

return buf
"""

# This tells Numba how to convert from its native representation
# of a DPArray in a njit function back to a Python DPArray.
@box(DPArrayType)
Expand Down Expand Up @@ -447,25 +357,18 @@ def numba_register_lower_builtin():
# For all Numpy identifiers that have been registered for typing in Numba...
# this registry contains functions, getattrs, setattrs, casts and constants...need to do them all? FIX FIX FIX
for ig in lower_registry.functions:
# print("ig:", ig, type(ig), len(ig))
impl, func, types = ig
# print("register lower_builtin:", impl, type(impl), func, type(func), types, type(types))
# If it is a Numpy function...
if isinstance(func, ftype):
# print("isfunction:", func.__module__, type(func.__module__))
if func.__module__ == np.__name__:
# print("name:", func.__name__)
# If we have overloaded that function in the dparray module (always True right now)...
if func.__name__ in functions_list:
todo.append(ig)
if isinstance(func, bftype):
# print("isbuiltinfunction:", func.__module__, type(func.__module__))
if func.__module__ == np.__name__:
# print("name:", func.__name__)
# If we have overloaded that function in the dparray module (always True right now)...
if func.__name__ in functions_list:
todo.append(ig)
# print("todo_builtin added:", func.__name__)

for lg in lower_registry.getattrs:
func, attr, types = lg
Expand All @@ -482,7 +385,6 @@ def numba_register_lower_builtin():
dparray_func = eval(func.__name__)
dprint("need to re-register lowerer for dparray", impl, func, types, dparray_func)
new_impl = copy_func_for_dparray(impl, cur_mod)
# lower_registry.functions.append((impl, dparray_func, types))
lower_registry.functions.append((new_impl, dparray_func, types))

def argspec_to_string(argspec):
Expand All @@ -500,10 +402,8 @@ def numba_register_typing():
# For all Numpy identifiers that have been registered for typing in Numba...
for ig in typing_registry.globals:
val, typ = ig
# print("global typing:", val, type(val), typ, type(typ))
# If it is a Numpy function...
if isinstance(val, (ftype, bftype)):
# print("name:", val.__name__, val.__name__ in functions_list)
# If we have overloaded that function in the dparray module (always True right now)...
if val.__name__ in functions_list:
todo.append(ig)
Expand All @@ -514,12 +414,6 @@ def numba_register_typing():
if tgetattr.key == types.Array:
todo_getattr.append(tgetattr)

# This is actuallya no-op now.
# for val, typ in todo_classes:
# print("todo_classes:", val, type(val), typ, type(typ))
# assert len(typ.templates) == 1
# dpval = eval(val.__name__)

for val, typ in todo:
assert len(typ.templates) == 1
# template is the typing class to invoke generic() upon.
Expand Down Expand Up @@ -549,9 +443,7 @@ def set_key_original(cls, key, original):
cls.original = original

def generic_impl(self):
# print("generic_impl", self.__class__.key, self.__class__.original)
original_typer = self.__class__.original.generic(self.__class__.original)
#print("original_typer:", original_typer, type(original_typer), self.__class__)
ot_argspec = inspect.getfullargspec(original_typer)
#print("ot_argspec:", ot_argspec)
astr = argspec_to_string(ot_argspec)
Expand Down