DAFunctor is a symbolic translator that converts NumPy/PyTorch ND-Array operations to equivalent C code. It works by decomposing generative NumPy functions into common properties like value expressions, index expressions, and scatter expressions. This allows DAFunctor to merge operations and generate loop-based C code without dynamic memory allocation or intermediate buffers.

1. DAFunctor
Symbolic Translator
from NumPy/PyTorch ND-Array operations to C
Buganini Chiu
PyCon Taiwan 2021
https://github.com/buganini/dafunctor

2. About me
●
Buganini Chiu, aka Bug
●
Know some C & Python
●
Had done a few deep learning solution porting tasks to various
platforms
https://github.com/buganini

3. What? Why? How?
●
Purpose
●
Other approaches
●
DAFunctor

4. Purpose
●
Porting numpy operations to certain platforms
– Memory limit for embedded systems
●
Intermediate buffers
– Lack of support for C++ and ecosystem (some RTOS; no POSIX API)
– Usage constrains ruled by development guidelines
●
Fragmentation concern
– MISRA C:2004, 20.4 - Dynamic heap memory allocation shall not be used.
– MISRA C++ 2008, 18-4-1 - Dynamic heap memory allocation shall not be used.
– MISRA C:2012, 21.3 The memory allocation and deallocation functions of <stdlib.h> shall not be used
– Interpolatability with other modules
●
Maintainability

5. Other approaches
Given a function like this :
def sample():
matrix = np.array([[4,5],[6,7]])
return matrix * 3 + 5

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*
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* return matrix * 3 + 5
*
*/
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* matrix = np.array([[4,5],[6,7]])
* return matrix * 3 + 5 # <<<<<<<<<<<<<<
*
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def sample():
matrix = np.array([[4,5],[6,7]])
return matrix * 3 + 5
Other approaches
Part of Cython output, auto translation
Not for alien systems

7. Other approaches
NumCpp, manual translation
#include "NumCpp.hpp"
nc::NdArray<int> sample() {
nc::NdArray<int> matrix = { {4, 5}, {6, 7} };
return matrix * 3 + 5;
}
def sample():
matrix = np.array([[4,5],[6,7]])
return matrix * 3 + 5
C++
Memory allocation for every function/operators

8. Other approaches
NumExpr, just for reference
import numpy as np
import numexpr as ne
def sample():
matrix = np.array([[4,5],[6,7]])
return ne.evaluate("matrix * 3 + 5")
def sample():
matrix = np.array([[4,5],[6,7]])
return matrix * 3 + 5
JIT only, does not generate code
Uses its own DSL for expression
No intermediate buffers

9. DAFunctor
auto translation
void sample(float array3[4] /* shape=[2, 2] */)
{
const static int d_array_1[] = {4,5,6,7};
for(int i0=0;i0<2;i0+=1)
for(int i1=0;i1<2;i1+=1)
{
array3[i0*2 + i1] = ((d_array_1[((i0*2)+i1)]*3)+5);
}
}
Exported to memory
def sample():
matrix = np.array([[4,5],[6,7]])
return matrix * 3 + 5
Plotted by DAFunctor

10. DAFunctor
auto translation
def sample():
A = np.array([[4,5],[6,7]]) * 2
B = np.array([[5,5],[6,6]]) + 3
return A + B
void sample(float array5[4] /* shape=[2, 2] */)
{
const static int d_array_1[] = {4,5,6,7};
const static int d_array_2[] = {5,5,6,6};
for(int i0=0;i0<2;i0+=1)
for(int i1=0;i1<2;i1+=1)
{
array5[i0*2 + i1] = ((d_array_1[((i0*2)+i1)]*2)+(d_array_2[((i0*2)+i1)]+3));
}
}
Exported to memory
Plotted by DAFunctor

11. Concepts
● Functor (函子) vs. Function (函數)
– How symbolic translation works
● Generative function (生成) vs. Aggregate function (匯總)
– What kind of functions is tackled in DAFunctor

12. Functor vs. Function
Functor (函子)
– Functor() → Function
– Constructs
●
Functions
●
Graphs, NN
– Example
●
functools.partial
●
torch.nn
Function (函數)
– Function() → Value
– Evaluates
– Example
●
Ordinary functions in
imperative programming
languages

13. Generative function vs. Aggregate function
Generative function (生成函數)
– Create from nothing
●
np.zeros, np.ones, np.arange, np.meshgrid
– 1 : 1
●
np.sqrt, arithmetics
●
np.transpose, np.reshape
– Merge
●
np.concatenate, np.stack
– 1 : N
●
np.repeat
– Slicing
– Most of them have common properties so that
they can be merged together.
Aggregate function (匯總函數)
– N : 1
●
np.max, np.min, np.mean,
np.std
Others
– Complex functions
– Branched logic

14. Decomposition of generative functions
The common properties
Take #1
– Consider functions like
●
np.ones, np.reshape,
arithmetics
for Didx in OutBuf:
OutBuf[Didx] = F( I(Didx) )
●
Intuitive

15. Decomposition of generative functions
The common properties
Take #1 cont.
– Consider functions like
●
np.ones, np.reshape,
arithmetics
●
np.stack, np.concatenate
for Didx in OutBuf:
k = find_source_parition()
OutBuf[Didx] = Fk( Ik(Didx) )
●
Intuitive
●
Branch in loop

16. Decomposition of generative functions
The common properties
Take #2
– Consider functions like
●
np.ones, np.reshape,
arithmetics
●
np.stack, np.concatenate
# stack/concat-ed partitions
for partition in partitions:
for Sidx in partition:
OutBuf[ J(Sidx) ] = F( Sidx )
●
No branch in loop

17. Decomposition of generative functions
The common properties
Take #2 cont.
– Consider functions like
●
np.ones, np.reshape,
arithmetics
●
np.stack, np.concatenate
●
np.repeat
# stack/concat-ed partitions
for partition in partitions:
for Sidx in partition:
OutBuf[ J(Sidx) ] = F( Sidx )
●
No branch in loop
●
Unable to handle scattering
(one-to-many indice mapping)

18. Decomposition of generative functions
The common properties
Take #3
– Consider functions like
●
np.ones, np.reshape,
arithmetics
●
np.stack, np.concatenate
●
np.repeat
# stack/concat-ed partitions
for partition in partitions:
for Sidx in partition:
Didx = J( Sidx )
v = F( Sidx )
for Kidx in scatters(Didx):
OutBuf[ Kidx ] = v

19. Decomposition of generative functions
The common properties
●
F : Value expression
– evaluate from index/data to value
●
J : Index expression
– evaluate from sub-functor index to index
for this functor
●
Scatters: Scatter expression
– map one index to multiple indices
●
Partitions : Sub-functors
●
Shape
– Start, num, step (for slicing)
# stack/concat-ed partitions
for partition in partitions:
for Sidx in partition:
Didx = J( Sidx )
v = F( Sidx )
for Kidx in scatters(Didx):
OutBuf[ Kidx ] = v

20. vexpr - ones, full
def ones(shape):
return NumpyFunctor(
shape,
vexpr = 1,
desc = "ones",
opdesc = f"ones({shape})",
)
def full(shape, fill_value):
return NumpyFunctor(
shape,
vexpr = fill_value,
desc = "full",
opdesc = f"full({shape}, {fill_value})",
)

21. vexpr - arange
shape = [int(math.ceil((end - start) / step))]
return NumpyFunctor(
shape,
vexpr = ["+",["d0",["*",["i0","d2"]]]], # start + i * step
data = [start, end, step],
desc = "arange",
opdesc = f"arange({start}, {end}, {step})",
)

22. def f():
a = [[1,2,3,4]]
return np.reshape( np.array(a), (2,2) )
iexpr - reshape
def reshape(cls, a, shape):
offset = ["+",[
["*",
[f"i{i}"] + [a.shape[j] for j in range(i+1,len(a.shape))]
] for i in rangel(a.shape)]]
iexpr = []
for i in rangel(shape):
iexpr.append(
ast_strip([
"//",
[
["%",
[offset]+[
[
"*",
shape[j:]
] for j in range(i+1)
]
],
["*", shape[i+1:]]]]))
Functor: #7 reshape3
reshape((2, 2))_array((1, 4))
shape=((0, 2, 1), (0, 2, 1))
partitions=[[(0, 1, 1), (0, 4, 1)]]
iexpr=[
['//', [['%', [['+', [['*', ['i0', 4]], 'i1']], ['*', [2, 2]]]], 2]]
['%', [['+', [['*', ['i0', 4]], 'i1']], ['*', [2, 2]], 2]]
]
Functor[0]: #6 array6
array((1, 4))
shape=((0, 1, 1), (0, 4, 1))
vexpr=['ref', ['d', ['+', [['*', ['i0', 4]], 'i1']]]]
data=[1, 2, 3, 4]

23. def f():
a = [[1,2,3,4]]
return np.reshape( np.array(a), (2,2) )
iexpr - reshape
void gen_reshape(float reshape3[4] /* shape=[2, 2] */)
{
const static int d_array_3[] = {1,2,3,4};
for(int i0=0;i0<1;i0+=1)
for(int i1=0;i1<4;i1+=1)
{
// reshape((2, 2))
const int i0_0_1 = ((((i0*4)+i1)%(2*2))/2);
const int i1_0_1 = (((i0*4)+i1)%(2*2)%2);
reshape3[i0_0_1*2 + i1_0_1] = d_array_3[((i0*4)+i1)];
}
}
Functor: #7 reshape3
reshape((2, 2))_array((1, 4))
shape=((0, 2, 1), (0, 2, 1))
partitions=[[(0, 1, 1), (0, 4, 1)]]
iexpr=[
['//', [['%', [['+', [['*', ['i0', 4]], 'i1']], ['*', [2, 2]]]], 2]]
['%', [['+', [['*', ['i0', 4]], 'i1']], ['*', [2, 2]], 2]]
]
Functor[0]: #6 array6
array((1, 4))
shape=((0, 1, 1), (0, 4, 1))
vexpr=['ref', ['d', ['+', [['*', ['i0', 4]], 'i1']]]]
data=[1, 2, 3, 4]

24. void gen_reshape(float reshape2[6] /* shape=[3, 2] */) {
const static int d_array_2[] = {1,2,3,4,5,6};
for(int i0=0;i0<2;i0+=1)
for(int i1=0;i1<3;i1+=1) {
// reshape((1, 2, 1, 3, 1))
const int i0_0_1 = ((((i0*3)+i1)%(1*2*1*3*1))/(2*1*3*1));
const int i1_0_1 = ((((i0*3)+i1)%(1*2*1*3*1)%(2*1*3*1))/(1*3*1));
const int i2_0_1 = ((((i0*3)+i1)%(1*2*1*3*1)%(2*1*3*1)%(1*3*1))/(3*1));
const int i3_0_1 = ((((i0*3)+i1)%(1*2*1*3*1)%(2*1*3*1)%(1*3*1)%(3*1))/1);
const int i4_0_1 = (((i0*3)+i1)%(1*2*1*3*1)%(2*1*3*1)%(1*3*1)%(3*1)%1);
// reshape((3, 2))
const int i0_0_2 = ((((i0_0_1*2*1*3*1)+(i1_0_1*1*3*1)+(i2_0_1*3*1)+(i3_0_1*1)+i4_0_1)%(3*2))/2);
const int i1_0_2 = (((i0_0_1*2*1*3*1)+(i1_0_1*1*3*1)+(i2_0_1*3*1)+(i3_0_1*1)+i4_0_1)%(3*2)%2);
reshape2[i0_0_2*2 + i1_0_2] = d_array_2[((i0*3)+i1)];
}
}
def f():
a = [[1,2,3],[4,5,6]]
return np.reshape( np.reshape( np.array(a), (1,2,1,3,1) ), (3,2) )
reshape - merged

25. def f():
return np.transpose(
np.array([[4,5],[6,7]]), (1,0)
) + 3
iexpr + vexpr
Functor: #3 transpose
add
shape=((0, 2, 1), (0, 2, 1))
vexpr=['+', ['v0', 3]]
Functor[0]: #2 array2
transposed_array((2, 2))
shape=((0, 2, 1), (0, 2, 1))
partitions=[[(0, 2, 1), (0, 2, 1)]]
iexpr=[
i1
i0
]
Functor[0]: #1 array1
array((2, 2))
shape=((0, 2, 1), (0, 2, 1))
vexpr=['ref', ['d', ['+', [['*', ['i0', 2]], 'i1']]]]
data=[4, 5, 6, 7]
void gen_transpose(float transpose[4] /* shape=[2, 2] */)
{
const static int d_array_1[] = {4,5,6,7};
for(int i0=0;i0<2;i0+=1)
for(int i1=0;i1<2;i1+=1)
{
// transpose((1, 0))
const int i0_0_1 = i1;
const int i1_0_1 = i0;
transpose[i0_0_1*2 + i1_0_1] = (d_array_1[((i0*2)+i1)]+3);
}
}

26. def f():
return np.repeat(
np.array( [[1,2,3],[4,5,6]]),
3, axis=1) sexpr - repeat
shape = list(a.shape)
shape[axis] *= repeats
iexpr = [f"i{i}" for i in rangel(shape)]
iexpr[axis] = ["*", [f"i{axis}", repeats]]
sexpr = (axis, 0, repeats, 1) # axis, start, num, step
return NumpyFunctor(
shape,
partitions = [[(0,s,1) for s in a.shape]],
iexpr = iexpr,
sexpr = sexpr,
desc = f"repeat_{repeats}",
opdesc = f"repeat({repeats})",
subs = [a]
)
Functor: #2 repeat_axis_1
repeat_3
shape=((0, 2, 1), (0, 9, 1))
partitions=[[(0, 2, 1), (0, 3, 1)]]
sexpr=(1, 0, 3, 1)
iexpr=[
i0
['*', ['i1', 3]]
]
Functor[0]: #1 array1
array((2, 3))
shape=((0, 2, 1), (0, 3, 1))
vexpr=['ref', ['d', ['+', [['*', ['i0', 3]], 'i1']]]]
data=[1, 2, 3, 4, 5, 6]

27. def f():
return np.repeat(
np.array( [[1,2,3],[4,5,6]]),
3, axis=1) sexpr - repeat
Functor: #2 repeat_axis_1
repeat_3
shape=((0, 2, 1), (0, 9, 1))
partitions=[[(0, 2, 1), (0, 3, 1)]]
sexpr=(1, 0, 3, 1)
iexpr=[
i0
['*', ['i1', 3]]
]
Functor[0]: #1 array1
array((2, 3))
shape=((0, 2, 1), (0, 3, 1))
vexpr=['ref', ['d', ['+', [['*', ['i0', 3]], 'i1']]]]
data=[1, 2, 3, 4, 5, 6]
void gen_repeat_axis_1(float repeat_axis_1[18] /* shape=[2, 9] */)
{
const static int d_array_1[] = {1,2,3,4,5,6};
for(int i0=0;i0<2;i0+=1)
for(int i1=0;i1<3;i1+=1)
{
// repeat(3)
const int i0_0_1 = i0;
const int i1_0_1 = (i1*3);
const float v0 = d_array_1[((i0*3)+i1)];
const int i0_1_1 = i0_0_1;
for(int i1_1_1=0+i1_0_1;i1_1_1<3+i1_0_1;i1_1_1+=1)
{
repeat_axis_1[i0_1_1*9 + i1_1_1] = v0;
}
}
}

28. Python Black Magic
●
Shadow object: data view wrapper
●
__assign__: let the object know its name
– To maintain readability
●
@jit decorator: wrap all the procedures translating python
function to native function

29. Shadow object: data view wrapper
●
Shape (1,12), (3,4), (6,2)
has no difference in
memory
●
Share everything except
for shape
– Once-only export
●
Override magic methods
– __getattr__
– __setattr__
class Reshaper(Functor):
def __init__(self, functor, shape):
self.functor = functor
if type(self.functor) is Reshaper:
self.functor = self.functor.functor
self.shape = Shape(shape)
def __getattr__(self, name):
if name in ("functor", "shape"):
return object.__getattr__(self, name)
else:
return getattr(self.functor, name)
def __setattr__(self, name, value):
if name in ("functor", "shape"):
object.__setattr__(self, name, value)
else:
setattr(self.functor, name, value)

30. __assign__: let the object know its name
Long story
def f(assignee):
a = assignee
b, c = assignee, assignee
arr = [assignee, assignee]
d, e = arr
f, (g, h) = assignee, (assignee, assignee)
i = [0,0]
i[1] = assignee
j = [0,0]
x = 1
j[x] = assignee
k = [0,0]
x = [1]
z = 0
k[x[z]] = assignee
https://github.com/buganini/DAFunctor/blob/master/dafunctor/assign.py

31. __assign__: let the object know its name
First thought
import inspect
def f():
a = set()
print("locals():", locals())
print("This frame:", inspect.currentframe().f_locals)
print("Outer frame:", inspect.currentframe().f_back.f_locals)
def f2():
b = set()
f()
f2()
●
Symbol table
– NumExpr, SymPy
●
Cannot be applied from the
outside of functions
Output:
locals(): {'a': set()}
This frame: {'a': set()}
Outer frame: {'b': set()}

32. __assign__: let the object know its name
Short story
varname = obj
varname = obj
if hasattr(obj, "__assign__"):
obj.__assign__("varname", None)
array[3] = obj
array[3] = obj
if hasattr(obj, "__assign__"):
obj.__assign__("array", 3)

33. __assign__: let the object know its name
1. Get the source code
2. Source code → AST (Abstract Syntax Tree)
3. Patch AST
4. Compile patched AST to bytecode
5. Execute bytecode

34. __assign__: let the object know its name
Get the source code
import inspect
def f():
a = set()
print(inspect.getsource(f))
def f():
a = set()

35. __assign__: let the object know its name
Source → AST (Abstract Syntax Tree)
import inspect
import ast
def f():
a = set()
src = inspect.getsource(f)
node = ast.parse(src)
# print(ast.dump(node, indent=4)) # Python 3.9+
print(ast.dump(node))
Module(
body=[
FunctionDef(
name='f',
args=arguments(
posonlyargs=[],
args=[],
kwonlyargs=[],
kw_defaults=[],
defaults=[]),
body=[
Assign(
targets=[
Name(id='a', ctx=Store())],
value=Call(
func=Name(id='set', ctx=Load()),
args=[],
keywords=[]))],
decorator_list=[])],
type_ignores=[])

36. __assign__: let the object know its name
Patch AST
class AssignTransformer(ast.NodeTransformer):
def visit_Assign(self, node):
...
def visit_FunctionDef(self, func):
…
trans = AssignTransformer()
new_node = trans.visit(node)
# print(ast.unparse(new_node)) # Python 3.9+
ast.fix_missing_locations(new_node)
print(ast.dump(new_node))

37. __assign__: let the object know its name
Patch AST
def f():
a = set()
Module(
body=[
FunctionDef(
name='f',
args=arguments(
posonlyargs=[],
args=[],
kwonlyargs=[],
kw_defaults=[],
defaults=[]),
body=[
Assign(
targets=[
Name(id='a', ctx=Store())],
value=Call(
func=Name(id='set', ctx=Load()),
args=[],
keywords=[]))],
decorator_list=[])],
type_ignores=[])
Assign(
targets=[
Name(id='a', ctx=Store())],
value=Call(
func=Name(id='set', ctx=Load()),
args=[],
keywords=[])),
If(
test=Call(
func=Name(id='hasattr', ctx=Load()),
args=[
Name(id='a', ctx=Load()),
Constant(value='__assign__')],
keywords=[]),
body=[
Expr(
value=Call(
func=Attribute(
value=Name(id='a', ctx=Load()),
attr='__assign__',
ctx=Load()),
args=[
Constant(value='a'),
Constant(value=None)],
keywords=[]))],
orelse=[])],

38. __assign__: let the object know its name
Patch AST
def f():
a = set()
# print(ast.unparse(new_node)) # Python 3.9+
def f():
if True:
a = set()
if hasattr(a, '__assign__'):
a.__assign__('a', None)
NodeTransformer does node-to-node transformation,
use a dummy IF to wrap two nodes.

39. __assign__: let the object know its name
Compile patched AST to bytecode
Execute bytecode
patched_code = compile(new_node, "__assign__", "exec")
local_vars = {}
exec(patched_code, global_vars, local_vars)
patched_func = local_vars[func.__name__]

40. __assign__: let the object know its name
def f(assignee):
a = assignee
b, c = assignee, assignee
arr = [assignee,assignee]
d, e = arr
f, (g, h) = assignee, (assignee,assignee)
i = [0,0]
i[1] = assignee
j = [0,0]
x = 1
j[x] = assignee
k = [0,0]
x = [1]
z = 0
k[x[z]] = assignee
Assign <__main__.A object at 0x7f5fa0ecc550> as assignee idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as a idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as b idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as c idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as d idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as e idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as f idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as g idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as h idx None
Assign <__main__.A object at 0x7f5fa0ecc550> as i idx 1
Assign <__main__.A object at 0x7f5fa0ecc550> as j idx 1
Assign <__main__.A object at 0x7f5fa0ecc550> as k idx 1
def __assign__(self, name, idx):
print("Assign", self, "as", name, "idx", idx)

41. @jit decorator
1. Generate C code for wrapped python function
2. Compile C code into .so (shared object)
3. Load native function from .so with ctypes or cffi
4. Wrap native function with data conversion wrapper
5. Call wrapper function when invoked

42. @jit decorator
Plain python function
func

43. @jit decorator
Add a dummy decorator
func
Decorator

44. @jit decorator
Generate C code for wrapped python function
func
Decorator
C

45. @jit decorator
Compile C code into .so (shared object)
func
Decorator
.so
C

46. @jit decorator
Load native function from .so with ctypes or cffi
func
Decorator
native
func
.so
C

47. @jit decorator
Wrap native function with data conversion wrapper
func
Decorator
native
func
pre post
.so
C

48. @jit decorator
Call wrapper function when invoked
func
Decorator
native
func
pre post
.so
C

49. @jit decorator
Python function has no power here
func
Decorator
native
func
pre post
.so
C

50. Python Black Magic
Pitfalls
●
Decorators come with function source code obtained with
inspect.getsource() and attach on function in AST
– Beware of decorator recursion during bytecode execution
●
Indentations come with function source code obtained with
inspect.getsource()
– Need to strip extra indentation before ast.parse()
●
AST structure differs with python versions
– Tested with 3.8, 3.9

51. The rest of the transpiler
sketchy version
Trace output nodes to get DAG (Directed Acyclic Graph)
– Unrelated parts are ignored

52. The rest of the transpiler
sketchy version
Split branched DAG (partitions)
# stack/concat-ed partitions
for partition in partitions:
for Sidx in partition:
Didx = J( Sidx )
v = F( Sidx )
for Kidx in scatters(Didx):
OutBuf[ Kidx ] = v

53. The rest of the transpiler
sketchy version
Assemble CFG (Control-Flow Graph) and AST according to
Iexpr/Vexpr/Sexpr for each unbranched DAG
['autobuf', Functor(id=2, name=A, desc=multiply, shape=((0, 2, 1), (0, 2, 1)), subs=1)],
['for_shape', ((0, 2, 1), (0, 2, 1)), 0, 0],
['scope',
[
[
'=',
Functor(id=2, name=A, desc=multiply, shape=((0, 2, 1), (0, 2, 1)), subs=1),
['*', [['ref', 'd_array_1', ('+', [['*', [['idx', 0, 0, 0], 2]], ['idx', 1, 0, 0]])], 2]], 0, 0
]
]
],
['newline'],
['comment', 'end of A'],

54. The rest of the transpiler
sketchy version
Generate C code
['autobuf', Functor(id=2, name=A, desc=multiply, shape=((0, 2, 1), (0, 2, 1)), subs=1)],
['for_shape', ((0, 2, 1), (0, 2, 1)), 0, 0],
['scope',
[
[
'=',
Functor(id=2, name=A, desc=multiply, shape=((0, 2, 1), (0, 2, 1)), subs=1),
['*', [['ref', 'd_array_1', ('+', [['*', [['idx', 0, 0, 0], 2]], ['idx', 1, 0, 0]])], 2]], 0, 0
]
]
],
['newline'],
['comment', 'end of A'],
AUTOBUF float A[2 * 2]; // [2, 2] multiply
for(int i0=0;i0<2;i0+=1)
for(int i1=0;i1<2;i1+=1)
{
A[i0*2 + i1] = (d_array_1[((i0*2)+i1)]*2);
}
// end of A

55. The rest of the transpiler
A bit more detail
Index tailoring
def f_range_step2(np):
a = list(range(20))
return np.array(a)[2:-2:2][3:15:3]
void gen_getitem_range_step2(float getitem_range_step2[2] /* shape=[2] */)
{
const static int d_array_1[] = {0,1,2,3,4,5,6,7,8,9,10,
11,12,13,14,15,16,17,18,19};
for(int i0=8;i0<20;i0+=6)
{
// [(slice(2, -2, 2),)]
const int i0_0_1 = ((i0-2)/2);
// [(slice(3, 15, 3),)]
const int i0_0_2 = ((i0_0_1-3)/3);
getitem_range_step2[i0_0_2] = d_array_1[i0];
}
}
Functor: #3 getitem_range_step2
array((20,))[(slice(2, -2, 2),)][(slice(3, 15, 3),)]
shape=((0, 2, 1),)
partitions=[[(3, 2, 3)]]
iexpr=[
['//', [['-', ['i0', 3]], 3]]
]
Functor[0]: #2 array2
array((20,))[(slice(2, -2, 2),)]
shape=((0, 8, 1),)
partitions=[[(2, 8, 2)]]
iexpr=[
['//', [['-', ['i0', 2]], 2]]
]
Functor[0]: #1 array1
array((20,))
shape=((0, 20, 1),)
vexpr=['ref', ['d', 'i0']]
data=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]

56. Testing System
NumPy/Torch
vs.
Functor I/V/S expr
vs.
Generated C code
Functor + Transpiler = Fragile Development

57. Testing System
Golden Result
Compare
(Verify functor) Compare
(Verify transpiler)
test
NumPy
functors
DaFunctor
Result
eval
I/V/S expr
C
transpile
.so
compile
Result2
execute

58. Testing System
Dependency Injection
– np
●
numpy
●
dafunctor.numpy
from tester_numpy import *
def sample(np):
A = np.array([[4,5],[6,7]]) * 2
B = np.array([[5,5],[6,6]]) + 3
return A + B
test_func("sample", sample)

59. Testing System
Transpiled evaluation
– As previously described
Pure Python evaluation
– Per-node evaluation
– Standalone interpreter

60. Testing System
import dafunctor.numpy as np
s = np.meshgrid([1,2],[3,4,5])
s.eval()
array([[[1., 2.],
[1., 2.],
[1., 2.]],
[[3., 3.],
[4., 4.],
[5., 5.]]])
f = s.jit()
f()
array([[[1., 2.],
[1., 2.],
[1., 2.]],
[[3., 3.],
[4., 4.],
[5., 5.]]], dtype=float32)

61. More python
importlib
– Test suite loader
pygments
– Syntax highlighter

62. importlib
Test suite loader
import importlib
for fn in sorted(os.listdir(os.path.dirname(os.path.abspath(__file__)))):
if fn.startswith("numpy_"):
try:
importlib.import_module(os.path.splitext(fn)[0])
except:
print("Error running test", fn)
# raise

63. pygments
Syntax highlighter

64. pygments
Syntax highlighter
from pygments import highlight
from pygments.formatters import TerminalFormatter as Formatter
from pygments.lexers import CLexer as Lexer
code = open(cfile).read()
print(highlight(code, Lexer(), Formatter()))
from pygments import highlight
from pygments.formatters import TerminalFormatter as Formatter
from pygments.lexers import CLexer as Lexer
code = open(cfile).read()
print(highlight(code, Lexer(), Formatter()))
https://extensions.libreoffice.org/en/extensions/show/code-highlighter

65. References
● https://devguide.python.org/compiler/
Design of CPython’s Compiler
– Concepts for transpiler design
● https://github.com/RyanKung/assign
– The origin of the __assign__ magic

66. Source of Inspiration
DaFunctor
– Generates C code
– Compiled to machine code
– Execute machine code
Python
– Generates bytecode
– Interpret bytecode
Julia (LLVM based JIT)
– Generates LLIR
– Compiled to machine code
– Execute machine code

67. Thank You