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Implement multi-output Elemwise in Numba via guvectorize #1271

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Implement multi-output Elemwise in Numba via guvectorize #1271

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Implement multi-output Elemwise in Numba via guvectorize
ricardoV94 Oct 18, 2022
8c1ff36
Try to get rid of looping, but this leads to NameError: global name '…
ricardoV94 Oct 26, 2022
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52 changes: 51 additions & 1 deletion aesara/link/numba/dispatch/elemwise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -162,6 +162,53 @@ def create_vectorize_func(
return elemwise_fn


def create_guvectorize_func(
scalar_op_fn: Callable,
node: Apply,
identity: Optional[Any] = None,
**kwargs,
) -> Callable:
r"""Create a guvectorized Numba function from a `Apply`\s Python function."""

signature_ = create_numba_signature(node, force_scalar=False)
signature = [(*signature_.args, *signature_.return_type.types)]

target = (
getattr(node.tag, "numba__vectorize_target", None)
or config.numba__vectorize_target
)

layout = f"{','.join(('()',) * len(node.inputs))}->{','.join(('()',) * len(node.outputs))}"
print(f"{signature=}, {layout=}")
numba_guvectorized_fn = numba.guvectorize(
signature,
layout,
identity=identity,
target=target,
fastmath=config.numba__fastmath,
)

input_names = [f"i{i}" for i in range(len(node.inputs))]
output_names = [f"o{i}" for i in range(len(node.outputs))]
gu_fn_name = "gu_func"
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Not familiar with the auto-naming strategy we have going on with Numba, are there any developer docs I can use as a reference?

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We should only need to make sure that the names we generate are fixed and don't clobber each other. As long as you're generating all the names yourself everything should be fine; it's usually when you're using unknown names provided by something else that problems start to arise.

On a very related note, if we want certain type of caching to work (e.g. the kind that's based on hashes of source code), we'll need to clean up some old code that uses Variable.name, and anything else that could differ between equivalent graphs. Since most of the unique name-based code was used to avoid Variable.name issues, we can probably drop all of it now. In summary, it might be useful for debugging and readability, but it's not necessary and it can negatively affect caching, so don't worry about it.


gu_fn_src = f"""
def {gu_fn_name}({', '.join(input_names)}, {', '.join(output_names)}):
for i in range({input_names[0]}.shape[0]):
{'[i], '.join(output_names)}[i] = scalar_op_fn({'[i], '.join(input_names)}[i])
"""
print(gu_fn_src)
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This basically creates a function that looks like:

def gu_func(i0, i1, ..., iN, o0, o1, ..., oN):
  for i in range(i0.shape[0]):
    o0[i], o1[i], ..., oN[i] = scalar_op_fn(i0[i], i1[i], ..., iN[i])

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@ricardoV94 ricardoV94 Oct 19, 2022 •
edited
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This also only seems to work for vector inputs. Am I supposed to do a nested loop for every dimension, or is there a shortcut/helper I can use?

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This is outdated now! I shouldn't need the loop at all


gu_fn_inner = compile_function_src(
gu_fn_src, gu_fn_name, {"scalar_op_fn": scalar_op_fn, **globals()}
)

gu_fn = numba_guvectorized_fn(gu_fn_inner)
# gu_fn.py_scalar_func = py_scalar_func

return gu_fn


def create_axis_reducer(
scalar_op: Op,
identity: Union[np.ndarray, Number],
Expand Down Expand Up @@ -426,7 +473,10 @@ def axis_apply_fn(x):
def numba_funcify_Elemwise(op, node, **kwargs):

scalar_op_fn = numba_funcify(op.scalar_op, node=node, inline="always", **kwargs)
elemwise_fn = create_vectorize_func(scalar_op_fn, node, use_signature=False)
if node.outputs == 1:
elemwise_fn = create_vectorize_func(scalar_op_fn, node, use_signature=False)
else:
elemwise_fn = create_guvectorize_func(scalar_op_fn, node)
elemwise_fn_name = elemwise_fn.__name__

if op.inplace_pattern:
Expand Down
1 change: 1 addition & 0 deletions aesara/link/numba/linker.py
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Expand Up @@ -27,6 +27,7 @@ def fgraph_convert(self, fgraph, **kwargs):
return numba_funcify(fgraph, **kwargs)

def jit_compile(self, fn):
return fn
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My test errors out when the jitting of the whole function is attempted:

E           numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
E           Untyped global name 'gu_func': Cannot determine Numba type of <class 'numba.np.ufunc.gufunc.GUFunc'>
E           
E           File "../../../../../../tmp/tmphopvpths", line 3:
E           def numba_funcified_fgraph(tensor_variable):
E               <source elided>
E                   # Elemwise{Composite{exp(i0), log(i0)}}(<TensorType(float64, (None,))>)
E               tensor_variable_1, tensor_variable_2 = gu_func(tensor_variable)

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Probably related to numba/numba#2089

import numba

jitted_fn = numba.njit(fn)
Expand Down
2 changes: 1 addition & 1 deletion tests/link/numba/test_basic.py
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Expand Up @@ -233,7 +233,7 @@ def assert_fn(x, y):
numba_res = aesara_numba_fn(*inputs)

# Get some coverage
eval_python_only(fn_inputs, fn_outputs, inputs, mode=numba_mode)
# eval_python_only(fn_inputs, fn_outputs, inputs, mode=numba_mode)
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This fails for reasons I haven't explored. It seems like it may need special logic for the multi-output Elemwises?


if len(fn_outputs) > 1:
for j, p in zip(numba_res, py_res):
Expand Down
17 changes: 17 additions & 0 deletions tests/link/numba/test_elemwise.py
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Expand Up @@ -3,6 +3,7 @@
import numpy as np
import pytest

import aesara.scalar as aes
import aesara.tensor as at
import aesara.tensor.inplace as ati
import aesara.tensor.math as aem
Expand All @@ -13,6 +14,7 @@
from aesara.graph.basic import Constant
from aesara.graph.fg import FunctionGraph
from aesara.tensor import elemwise as at_elemwise
from aesara.tensor.elemwise import Elemwise
from aesara.tensor.math import All, Any, Max, Mean, Min, Prod, ProdWithoutZeros, Sum
from tests.link.numba.test_basic import (
compare_numba_and_py,
Expand Down Expand Up @@ -111,6 +113,21 @@ def test_Elemwise(inputs, input_vals, output_fn, exc):
compare_numba_and_py(out_fg, input_vals)


def test_multioutput_elemwise():
scalar_inp = aes.float64()
scalar_out1 = aes.exp(scalar_inp)
scalar_out2 = aes.log(scalar_inp)
scalar_composite = aes.Composite([scalar_inp], [scalar_out1, scalar_out2])

tensor_inp = at.dvector()
tensor_outs = Elemwise(scalar_composite)(tensor_inp)

out_fg = FunctionGraph([tensor_inp], tensor_outs)

print("")
compare_numba_and_py(out_fg, [np.r_[1.0, 2.0, 3.5]])


@pytest.mark.parametrize(
"v, new_order",
[
Expand Down