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adrianlizarraga merged 2 commits into from
Sep 24, 2025
Merged

Regenerate test model with ONNX IR < 12 #26149

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2a3ff3a
Regenerate test model with ONNX IR < 12
adrianlizarraga Sep 24, 2025
7c2102d
Remove npy files and embed weight values within python scripts
adrianlizarraga Sep 24, 2025
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Binary file modified onnxruntime/test/testdata/input_propagated_to_output.onnx
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113 changes: 113 additions & 0 deletions onnxruntime/test/testdata/input_propagated_to_output.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,113 @@
"""
Run this script to recreate the original onnx model.
Example usage:
python input_propagated_to_output.py input_propagated_to_output.onnx
"""

import sys

import numpy as np
import onnx


def order_repeated_field(repeated_proto, key_name, order):
order = list(order)
repeated_proto.sort(key=lambda x: order.index(getattr(x, key_name)))


def make_node(op_type, inputs, outputs, name=None, doc_string=None, domain=None, **kwargs):
node = onnx.helper.make_node(op_type, inputs, outputs, name, doc_string, domain, **kwargs)
if doc_string == "":
node.doc_string = ""
order_repeated_field(node.attribute, "name", kwargs.keys())
return node


def make_graph(*args, doc_string=None, **kwargs):
graph = onnx.helper.make_graph(*args, doc_string=doc_string, **kwargs)
if doc_string == "":
graph.doc_string = ""
return graph


W1 = np.array(
[
[[[0.3258337378501892]], [[0.1461111307144165]], [[-0.4239698648452759]]],
[[[0.14769716560840607]], [[0.20565544068813324]], [[-0.5241780877113342]]],
[[[0.07987150549888611]], [[-0.17475983500480652]], [[0.005230882670730352]]],
],
dtype=np.float32,
)

B1 = np.array(
[-0.3170531392097473, -0.2701416313648224, -0.14249320328235626],
dtype=np.float32,
)

W3 = np.array(
[
[[[0.14025720953941345]], [[0.1433156430721283]], [[-0.1403128057718277]]],
[[[-0.07530076801776886]], [[0.11853527277708054]], [[-0.19437682628631592]]],
[[[0.5786639451980591]], [[-0.28565627336502075]], [[0.9048876166343689]]],
],
dtype=np.float32,
)

B3 = np.array(
[-0.13307525217533112, 0.5522456169128418, 0.6449958086013794],
dtype=np.float32,
)

W5 = np.array(
[
[[[-0.08959630876779556]], [[0.07607565075159073]], [[0.24446037411689758]]],
[[[-0.06293385475873947]], [[-0.41520264744758606]], [[-0.83400559425354]]],
[[[-0.031176576390862465]], [[-0.04187283664941788]], [[-0.439873069524765]]],
],
dtype=np.float32,
)

B5 = np.array(
[0.5949633717536926, -0.40198755264282227, -0.20182392001152039],
dtype=np.float32,
)

model = onnx.helper.make_model(
opset_imports=[onnx.helper.make_operatorsetid("", 14)],
ir_version=7,
graph=make_graph(
name="input_propagated_to_output",
inputs=[
onnx.helper.make_tensor_value_info("input", onnx.TensorProto.FLOAT, shape=[1, 3, 1, 3]),
],
outputs=[
onnx.helper.make_tensor_value_info("X6", onnx.TensorProto.FLOAT, shape=[1, 3, 1, 3]),
onnx.helper.make_tensor_value_info("X1", onnx.TensorProto.FLOAT, shape=[1, 3, 1, 3]),
onnx.helper.make_tensor_value_info("input", onnx.TensorProto.FLOAT, shape=[1, 3, 1, 3]),
onnx.helper.make_tensor_value_info("X2", onnx.TensorProto.FLOAT, shape=[1, 3, 1, 3]),
onnx.helper.make_tensor_value_info("X4", onnx.TensorProto.FLOAT, shape=[1, 3, 1, 3]),
onnx.helper.make_tensor_value_info("X3", onnx.TensorProto.FLOAT, shape=[1, 3, 1, 3]),
onnx.helper.make_tensor_value_info("X5", onnx.TensorProto.FLOAT, shape=[1, 3, 1, 3]),
],
initializer=[
onnx.numpy_helper.from_array(W1, name="W1"),
onnx.numpy_helper.from_array(W3, name="W3"),
onnx.numpy_helper.from_array(W5, name="W5"),
onnx.numpy_helper.from_array(B1, name="B1"),
onnx.numpy_helper.from_array(B3, name="B3"),
onnx.numpy_helper.from_array(B5, name="B5"),
],
nodes=[
make_node("Relu", inputs=["input"], outputs=["X1"], name="Relu1"),
make_node("Conv", inputs=["X1", "W1", "B1"], outputs=["X2"], name="Conv1"),
make_node("Relu", inputs=["X2"], outputs=["X3"], name="Relu2"),
make_node("Conv", inputs=["X3", "W3", "B3"], outputs=["X4"], name="Conv2"),
make_node("Conv", inputs=["X1", "W5", "B5"], outputs=["X5"], name="Conv3"),
make_node("Add", inputs=["X4", "X5"], outputs=["X6"], name="Add"),
],
),
)

if __name__ == "__main__" and len(sys.argv) == 2:
_, out_path = sys.argv
onnx.save(model, out_path)
96 changes: 73 additions & 23 deletions onnxruntime/test/testdata/test_dangling_input_segment_ids.py
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Original file line number Diff line number Diff line change
@@ -1,17 +1,13 @@
"""
Run this script to recreate the original onnx model.
Example usage:
python test_dangling_input_segment_ids.py out_model_path.onnx
python test_dangling_input_segment_ids.py test_dangling_input_segment_ids.onnx
"""

import os
import sys

import numpy as np
import onnx
from onnx import TensorProto, helper, numpy_helper

DATA_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_dangling_input_segment_ids")


def order_repeated_field(repeated_proto, key_name, order):
Expand All @@ -20,50 +16,104 @@ def order_repeated_field(repeated_proto, key_name, order):


def make_node(op_type, inputs, outputs, name=None, doc_string=None, domain=None, **kwargs):
node = helper.make_node(op_type, inputs, outputs, name, doc_string, domain, **kwargs)
node = onnx.helper.make_node(op_type, inputs, outputs, name, doc_string, domain, **kwargs)
if doc_string == "":
node.doc_string = ""
order_repeated_field(node.attribute, "name", kwargs.keys())
return node


def make_graph(*args, doc_string=None, **kwargs):
graph = helper.make_graph(*args, doc_string=doc_string, **kwargs)
graph = onnx.helper.make_graph(*args, doc_string=doc_string, **kwargs)
if doc_string == "":
graph.doc_string = ""
return graph


model = helper.make_model(
opset_imports=[helper.make_operatorsetid("", 14), helper.make_operatorsetid("com.microsoft", 1)],
WORD_EMBED = np.array(
[
[0.31524479389190674, 0.8928887248039246, 0.5778571963310242, 0.18401020765304565],
[0.7879292368888855, 0.6120311617851257, 0.05390927195549011, 0.4201936721801758],
[0.6790688633918762, 0.9186017513275146, 0.0004020248888991773, 0.976759135723114],
[0.3765803277492523, 0.973783552646637, 0.6047161221504211, 0.8288457989692688],
[0.5747115015983582, 0.6280761957168579, 0.2855762839317322, 0.5868333578109741],
[0.750021755695343, 0.8583138585090637, 0.7550821900367737, 0.698057234287262],
[0.8644794225692749, 0.3226810097694397, 0.6707887649536133, 0.4508739411830902],
[0.38210275769233704, 0.4108113646507263, 0.401479572057724, 0.31738394498825073],
[0.6219193935394287, 0.4302472770214081, 0.9738020896911621, 0.6778008937835693],
[0.1985698938369751, 0.42670100927352905, 0.3433462381362915, 0.7976388335227966],
[0.8799982666969299, 0.9038419723510742, 0.6627197861671448, 0.2702082693576813],
[0.25236669182777405, 0.8548979163169861, 0.5277146697044373, 0.8021610975265503],
[0.57248854637146, 0.7331425547599792, 0.5190116167068481, 0.7708839178085327],
[0.5688579678535461, 0.4657098650932312, 0.3426889181137085, 0.06820935010910034],
[0.3779241740703583, 0.07962607592344284, 0.9828171133995056, 0.18161284923553467],
[0.8118587136268616, 0.8749616742134094, 0.6884132623672485, 0.5694944262504578],
[0.16097143292427063, 0.46688002347946167, 0.34517204761505127, 0.22503995895385742],
[0.5925118923187256, 0.31226983666419983, 0.9163055419921875, 0.9096355438232422],
[0.257118284702301, 0.11089129745960236, 0.19296273589134216, 0.4995841681957245],
[0.7285856604576111, 0.20819443464279175, 0.2480335533618927, 0.8516718745231628],
[0.4158487319946289, 0.6166850924491882, 0.23366613686084747, 0.10196726024150848],
[0.5158570408821106, 0.47714099287986755, 0.15267165005207062, 0.6218062043190002],
[0.5440101027488708, 0.654137372970581, 0.1445455402135849, 0.7515278458595276],
[0.22204914689064026, 0.5193518400192261, 0.7852960228919983, 0.022330427542328835],
[0.32436245679855347, 0.8729223608970642, 0.8447096347808838, 0.5384405851364136],
[0.8666082620620728, 0.9498059749603271, 0.8264070153236389, 0.8541154265403748],
[0.09874340146780014, 0.651304304599762, 0.703516960144043, 0.6102408170700073],
[0.7996152639389038, 0.034571219235658646, 0.7702387571334839, 0.7317286133766174],
[0.25969839096069336, 0.25706928968429565, 0.6323032975196838, 0.3452974557876587],
[0.796588659286499, 0.4461462199687958, 0.7827494144439697, 0.9904717803001404],
[0.30024832487106323, 0.143005833029747, 0.9013084173202515, 0.5415593981742859],
[0.9747403860092163, 0.6366044282913208, 0.9939129948616028, 0.5460708141326904],
],
dtype=np.float32,
)

POS_EMBED = np.array(
[
[0.5264259576797485, 0.13542790710926056, 0.3557051718235016, 0.026218567043542862],
[0.16039517521858215, 0.7456371784210205, 0.030399689450860023, 0.36654308438301086],
[0.8623462319374084, 0.6926777362823486, 0.6909421682357788, 0.18863679468631744],
[0.4419042766094208, 0.5815774202346802, 0.9897516965866089, 0.20390622317790985],
[0.24773290753364563, 0.2621730864048004, 0.7501724362373352, 0.4569753408432007],
[0.056929439306259155, 0.508516252040863, 0.21196016669273376, 0.7986042499542236],
[0.29733139276504517, 0.027606012299656868, 0.5934324264526367, 0.8438404202461243],
[0.3810161352157593, 0.7498583197593689, 0.5111414790153503, 0.5409517884254456],
[0.9594343304634094, 0.803960919380188, 0.032323066145181656, 0.7093872427940369],
[0.46500149369239807, 0.9475489258766174, 0.22143273055553436, 0.26707202196121216],
[0.08147396147251129, 0.42861881852149963, 0.10901876538991928, 0.6337867379188538],
[0.8029632568359375, 0.6968004703521729, 0.7662113904953003, 0.34245410561561584],
[0.845851480960846, 0.4287687838077545, 0.824009895324707, 0.6264961361885071],
[0.14342305064201355, 0.07838690280914307, 0.018332643434405327, 0.0667250007390976],
[0.458583801984787, 0.11334192007780075, 0.0277833491563797, 0.7548614740371704],
[0.394850492477417, 0.7469384670257568, 0.45240482687950134, 0.4500867426395416],
],
dtype=np.float32,
)

model = onnx.helper.make_model(
opset_imports=[onnx.helper.make_operatorsetid("", 14), onnx.helper.make_operatorsetid("com.microsoft", 1)],
ir_version=7,
graph=make_graph(
name="embed_layernorm_graph",
inputs=[
helper.make_tensor_value_info("input_ids", TensorProto.INT32, shape=[1, 4]),
helper.make_tensor_value_info("segment_ids", TensorProto.INT32, shape=[1, 4]),
onnx.helper.make_tensor_value_info("input_ids", onnx.TensorProto.INT32, shape=[1, 4]),
onnx.helper.make_tensor_value_info("segment_ids", onnx.TensorProto.INT32, shape=[1, 4]),
],
outputs=[
helper.make_tensor_value_info("layernorm_out", TensorProto.FLOAT, shape=[1, 4, 4]),
helper.make_tensor_value_info("mask_index_out", TensorProto.INT32, shape=[1]),
onnx.helper.make_tensor_value_info("layernorm_out", onnx.TensorProto.FLOAT, shape=[1, 4, 4]),
onnx.helper.make_tensor_value_info("mask_index_out", onnx.TensorProto.INT32, shape=[1]),
],
initializer=[
numpy_helper.from_array(
np.load(os.path.join(DATA_DIR, "const0_word_embed.npy")).astype("float32").reshape([32, 4]),
name="word_embed",
),
numpy_helper.from_array(
np.load(os.path.join(DATA_DIR, "const1_pos_embed.npy")).astype("float32").reshape([16, 4]),
name="pos_embed",
),
numpy_helper.from_array(
onnx.numpy_helper.from_array(WORD_EMBED, name="word_embed"),
onnx.numpy_helper.from_array(POS_EMBED, name="pos_embed"),
onnx.numpy_helper.from_array(
np.array(
[0.6185135841369629, 0.010364261455833912, 0.5386272668838501, 0.0030179566238075495],
dtype="float32",
),
name="gamma",
),
numpy_helper.from_array(
onnx.numpy_helper.from_array(
np.array(
[0.9511938095092773, 0.9054020047187805, 0.7959669232368469, 0.9152743220329285], dtype="float32"
),
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