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Adds code rendering when converting a model into code#104

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sdpython merged 5 commits intomainfromoh
Nov 30, 2025
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7 changes: 6 additions & 1 deletionCHANGELOGS.rst
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Original file line numberDiff line numberDiff line change
@@ -1,10 +1,15 @@
Change Logs
===========

0.3.2
0.3.3
+++++

* :pr:`104`: add code rendering when conveting a model into code
* :pr:`103`: fix import issue with the latest onnx version

0.3.2
+++++

* :pr:`101`: fix as_tensor in onnx_text_plot_tree

0.3.1
Expand Down
7 changes: 7 additions & 0 deletions_unittests/ut_light_api/test_backend_export.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,12 +4,15 @@
from difflib import unified_diff
import packaging.version as pv
import numpy
import ml_dtypes
from numpy.testing import assert_allclose
from onnx.defs import onnx_opset_version
import onnx.backend.base
import onnx.backend.test
import onnx.shape_inference
import onnx.version_converter
import onnx.helper as oh
import onnx.numpy_helper as onh
from onnx import ModelProto, TensorProto, __version__ as onnx_version
from onnx.helper import (
make_function,
Expand DownExpand Up@@ -94,6 +97,10 @@ def run(

locs = {
"np": numpy,
"ml_dtypes": ml_dtypes,
"onnx": onnx,
"oh": oh,
"onh": onh,
"to_array": to_array,
"to_array_extended": to_array_extended,
"from_array": from_array,
Expand Down
49 changes: 27 additions & 22 deletions_unittests/ut_translate_api/test_translate.py
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Original file line numberDiff line numberDiff line change
@@ -1,12 +1,12 @@
import unittest
from textwrap import dedent
import numpy as np
from onnximportModelProto, TensorProto
importonnx
from onnx.defs import onnx_opset_version
from onnx.reference import ReferenceEvaluator
from onnx_array_api.ext_test_case import ExtTestCase
from onnx_array_api.light_api import start, g
from onnx_array_api.translate_api import translate
from onnx_array_api.translate_api import translate, translate_header
from onnx_array_api.translate_api.base_emitter import EventType

OPSET_API = min(19, onnx_opset_version() - 1)
Expand All@@ -18,9 +18,14 @@ def test_event_type(self):
EventType.to_str(EventType.INITIALIZER), "EventType.INITIALIZER"
)

def test_translate_header(self):
for f in ["light", "onnx", "builder"]:
translate_header(f)
self.assertRaise(lambda: translate_header("NONE"), ValueError)

def test_exp(self):
onx = start(opset=19).vin("X").Exp().rename("Y").vout().to_onnx()
self.assertIsInstance(onx, ModelProto)
self.assertIsInstance(onx,onnx.ModelProto)
self.assertIn("Exp", str(onx))
ref = ReferenceEvaluator(onx)
a = np.arange(10).astype(np.float32)
Expand All@@ -32,25 +37,25 @@ def test_exp(self):
"""
(
start(opset=19)
.vin('X', elem_type=TensorProto.FLOAT)
.vin('X', elem_type=onnx.TensorProto.FLOAT)
.bring('X')
.Exp()
.rename('Y')
.bring('Y')
.vout(elem_type=TensorProto.FLOAT)
.vout(elem_type=onnx.TensorProto.FLOAT)
.to_onnx()
)"""
).strip("\n")
self.assertEqual(expected, code)

onx2 = (
start(opset=19)
.vin("X", elem_type=TensorProto.FLOAT)
.vin("X", elem_type=onnx.TensorProto.FLOAT)
.bring("X")
.Exp()
.rename("Y")
.bring("Y")
.vout(elem_type=TensorProto.FLOAT)
.vout(elem_type=onnx.TensorProto.FLOAT)
.to_onnx()
)
ref = ReferenceEvaluator(onx2)
Expand All@@ -68,7 +73,7 @@ def test_transpose(self):
.vout()
.to_onnx()
)
self.assertIsInstance(onx, ModelProto)
self.assertIsInstance(onx,onnx.ModelProto)
self.assertIn("Transpose", str(onx))
ref = ReferenceEvaluator(onx)
a = np.arange(10).astype(np.float32)
Expand All@@ -82,15 +87,15 @@ def test_transpose(self):
start(opset=19)
.cst(np.array([-1, 1], dtype=np.int64))
.rename('r')
.vin('X', elem_type=TensorProto.FLOAT)
.vin('X', elem_type=onnx.TensorProto.FLOAT)
.bring('X', 'r')
.Reshape()
.rename('r0_0')
.bring('r0_0')
.Transpose(perm=[1, 0])
.rename('Y')
.bring('Y')
.vout(elem_type=TensorProto.FLOAT)
.vout(elem_type=onnx.TensorProto.FLOAT)
.to_onnx()
)"""
).strip("\n")
Expand All@@ -107,7 +112,7 @@ def test_topk_reverse(self):
.vout()
.to_onnx()
)
self.assertIsInstance(onx, ModelProto)
self.assertIsInstance(onx,onnx.ModelProto)
ref = ReferenceEvaluator(onx)
x = np.array([[0, 1, 2, 3], [9, 8, 7, 6]], dtype=np.float32)
k = np.array([2], dtype=np.int64)
Expand All@@ -120,15 +125,15 @@ def test_topk_reverse(self):
"""
(
start(opset=19)
.vin('X', elem_type=TensorProto.FLOAT)
.vin('K', elem_type=TensorProto.INT64)
.vin('X', elem_type=onnx.TensorProto.FLOAT)
.vin('K', elem_type=onnx.TensorProto.INT64)
.bring('X', 'K')
.TopK(axis=-1, largest=0, sorted=1)
.rename('Values', 'Indices')
.bring('Values')
.vout(elem_type=TensorProto.FLOAT)
.vout(elem_type=onnx.TensorProto.FLOAT)
.bring('Indices')
.vout(elem_type=TensorProto.FLOAT)
.vout(elem_type=onnx.TensorProto.FLOAT)
.to_onnx()
)"""
).strip("\n")
Expand All@@ -152,7 +157,7 @@ def test_export_if(self):
.to_onnx()
)

self.assertIsInstance(onx, ModelProto)
self.assertIsInstance(onx,onnx.ModelProto)
ref = ReferenceEvaluator(onx)
x = np.array([[0, 1, 2, 3], [9, 8, 7, 6]], dtype=np.float32)
k = np.array([2], dtype=np.int64)
Expand All@@ -162,19 +167,19 @@ def test_export_if(self):
code = translate(onx)
selse = (
"g().cst(np.array([0], dtype=np.int64)).rename('Z')."
"bring('Z').vout(elem_type=TensorProto.FLOAT)"
"bring('Z').vout(elem_type=onnx.TensorProto.FLOAT)"
)
sthen = (
"g().cst(np.array([1], dtype=np.int64)).rename('Z')."
"bring('Z').vout(elem_type=TensorProto.FLOAT)"
"bring('Z').vout(elem_type=onnx.TensorProto.FLOAT)"
)
expected = dedent(
f"""
(
start(opset=19)
.cst(np.array([0.0], dtype=np.float32))
.rename('r')
.vin('X', elem_type=TensorProto.FLOAT)
.vin('X', elem_type=onnx.TensorProto.FLOAT)
.bring('X')
.ReduceSum(keepdims=1, noop_with_empty_axes=0)
.rename('Xs')
Expand All@@ -185,7 +190,7 @@ def test_export_if(self):
.If(else_branch={selse}, then_branch={sthen})
.rename('W')
.bring('W')
.vout(elem_type=TensorProto.FLOAT)
.vout(elem_type=onnx.TensorProto.FLOAT)
.to_onnx()
)"""
).strip("\n")
Expand All@@ -210,15 +215,15 @@ def test_aionnxml(self):
start(opset=19, opsets={'ai.onnx.ml': 3})
.cst(np.array([-1, 1], dtype=np.int64))
.rename('r')
.vin('X', elem_type=TensorProto.FLOAT)
.vin('X', elem_type=onnx.TensorProto.FLOAT)
.bring('X', 'r')
.Reshape()
.rename('USE')
.bring('USE')
.ai.onnx.ml.Normalizer(norm='MAX')
.rename('Y')
.bring('Y')
.vout(elem_type=TensorProto.FLOAT)
.vout(elem_type=onnx.TensorProto.FLOAT)
.to_onnx()
)"""
).strip("\n")
Expand Down
46 changes: 23 additions & 23 deletions_unittests/ut_translate_api/test_translate_builder.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -2,7 +2,7 @@
from textwrap import dedent
import numpy as np
import onnx.helper as oh
from onnximportModelProto, TensorProto
importonnx
from onnx.checker import check_model
from onnx.defs import onnx_opset_version
from onnx.reference import ReferenceEvaluator
Expand All@@ -22,7 +22,7 @@ def setUp(self):

def test_exp(self):
onx = start(opset=19, ir_version=10).vin("X").Exp().rename("Y").vout().to_onnx()
self.assertIsInstance(onx, ModelProto)
self.assertIsInstance(onx,onnx.ModelProto)
self.assertIn("Exp", str(onx))
ref = ReferenceEvaluator(onx)
a = np.arange(10).astype(np.float32)
Expand All@@ -42,9 +42,9 @@ def light_api(
return Y

g = GraphBuilder({'': 19}, ir_version=10)
g.make_tensor_input("X", TensorProto.FLOAT, ())
g.make_tensor_input("X",onnx.TensorProto.FLOAT, ())
light_api(g.op, "X")
g.make_tensor_output("Y", TensorProto.FLOAT, ()__SUFFIX__)
g.make_tensor_output("Y",onnx.TensorProto.FLOAT, ()__SUFFIX__)
model = g.to_onnx()
"""
)
Expand All@@ -62,10 +62,10 @@ def light_api(
return Y

g2 = GraphBuilder({"": 19})
g2.make_tensor_input("X", TensorProto.FLOAT, ("A",))
g2.make_tensor_input("X",onnx.TensorProto.FLOAT, ("A",))
light_api(g2.op, "X")
g2.make_tensor_output(
"Y", TensorProto.FLOAT, ("A",), is_dimension=False, indexed=False
"Y",onnx.TensorProto.FLOAT, ("A",), is_dimension=False, indexed=False
)
onx2 = g2.to_onnx()

Expand DownExpand Up@@ -99,9 +99,9 @@ def light_api(
return Y

g = GraphBuilder({'': 19}, ir_version=10)
g.make_tensor_input("X", TensorProto.FLOAT, ())
g.make_tensor_input("X",onnx.TensorProto.FLOAT, ())
light_api(g.op, "X")
g.make_tensor_output("Y", TensorProto.FLOAT, ()__SUFFIX__)
g.make_tensor_output("Y",onnx.TensorProto.FLOAT, ()__SUFFIX__)
model = g.to_onnx()
"""
)
Expand All@@ -122,16 +122,16 @@ def light_api(
return Y

g = GraphBuilder({"": 21})
X = g.make_tensor_input("X", TensorProto.FLOAT, ())
X = g.make_tensor_input("X",onnx.TensorProto.FLOAT, ())
light_api(g.op, X)
g.make_tensor_output("Y", TensorProto.FLOAT, ())
g.make_tensor_output("Y",onnx.TensorProto.FLOAT, ())
model = g.to_onnx()
self.assertNotEmpty(model)
check_model(model)

def test_exp_f(self):
onx = start(opset=19, ir_version=10).vin("X").Exp().rename("Y").vout().to_onnx()
self.assertIsInstance(onx, ModelProto)
self.assertIsInstance(onx,onnx.ModelProto)
self.assertIn("Exp", str(onx))
ref = ReferenceEvaluator(onx)
a = np.arange(10).astype(np.float32)
Expand All@@ -155,9 +155,9 @@ def light_api(

def mm() -> "ModelProto":
g = GraphBuilder({'': 19}, ir_version=10)
g.make_tensor_input("X", TensorProto.FLOAT, ())
g.make_tensor_input("X",onnx.TensorProto.FLOAT, ())
light_api(g.op, "X")
g.make_tensor_output("Y", TensorProto.FLOAT, ()__SUFFIX__)
g.make_tensor_output("Y",onnx.TensorProto.FLOAT, ()__SUFFIX__)
model = g.to_onnx()
return model

Expand All@@ -179,10 +179,10 @@ def light_api(
return Y

g2 = GraphBuilder({"": 19})
g2.make_tensor_input("X", TensorProto.FLOAT, ("A",))
g2.make_tensor_input("X",onnx.TensorProto.FLOAT, ("A",))
light_api(g2.op, "X")
g2.make_tensor_output(
"Y", TensorProto.FLOAT, ("A",), is_dimension=False, indexed=False
"Y",onnx.TensorProto.FLOAT, ("A",), is_dimension=False, indexed=False
)
onx2 = g2.to_onnx()

Expand DownExpand Up@@ -216,11 +216,11 @@ def test_local_function(self):
],
"example",
[
oh.make_tensor_value_info("X", TensorProto.FLOAT, [None, None]),
oh.make_tensor_value_info("A", TensorProto.FLOAT, [None, None]),
oh.make_tensor_value_info("B", TensorProto.FLOAT, [None, None]),
oh.make_tensor_value_info("X",onnx.TensorProto.FLOAT, [None, None]),
oh.make_tensor_value_info("A",onnx.TensorProto.FLOAT, [None, None]),
oh.make_tensor_value_info("B",onnx.TensorProto.FLOAT, [None, None]),
],
[oh.make_tensor_value_info("Y", TensorProto.FLOAT, None)],
[oh.make_tensor_value_info("Y",onnx.TensorProto.FLOAT, None)],
)

onnx_model = oh.make_model(
Expand DownExpand Up@@ -262,11 +262,11 @@ def make_custom_LinearRegression(g: "GraphBuilder"):

def mm() -> "ModelProto":
g = GraphBuilder({'': 14, 'custom': 1}, ir_version=10)
g.make_tensor_input("X", TensorProto.FLOAT, ('', ''))
g.make_tensor_input("A", TensorProto.FLOAT, ('', ''))
g.make_tensor_input("B", TensorProto.FLOAT, ('', ''))
g.make_tensor_input("X",onnx.TensorProto.FLOAT, ('', ''))
g.make_tensor_input("A",onnx.TensorProto.FLOAT, ('', ''))
g.make_tensor_input("B",onnx.TensorProto.FLOAT, ('', ''))
example(g.op, "X", "A", "B")
g.make_tensor_output("Y", TensorProto.FLOAT, ()__SUFFIX__)
g.make_tensor_output("Y",onnx.TensorProto.FLOAT, ()__SUFFIX__)
make_custom_LinearRegression(g)
model = g.to_onnx()
return model
Expand Down
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