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+101
-37
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6 files changed

+101
-37
lines changed

‎_unittests/onnx-numpy-skips.txt

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,7 @@
44
array_api_tests/test_creation_functions.py::test_asarray_arrays
55
array_api_tests/test_creation_functions.py::test_empty
66
array_api_tests/test_creation_functions.py::test_empty_like
7+
array_api_tests/test_creation_functions.py::test_eye
78
# fails to precision issue
89
array_api_tests/test_creation_functions.py::test_linspace
910
array_api_tests/test_creation_functions.py::test_meshgrid

‎_unittests/ut_reference/test_array_tensor.py

Lines changed: 1 addition & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -1,13 +1,7 @@
11
import unittest
22
import numpy as np
33
from onnx import TensorProto
4-
from onnx.helper import (
5-
make_graph,
6-
make_model,
7-
make_node,
8-
make_tensor_value_info,
9-
make_opsetid,
10-
)
4+
from onnx.helper import make_graph, make_model, make_node, make_tensor_value_info
115
from onnx_array_api.ext_test_case import ExtTestCase
126
from onnx_array_api.reference import (
137
to_array_extended,
@@ -57,24 +51,6 @@ def make_model_f8(fr, to):
5751
back = from_array_extended(got, "a")
5852
self.assertEqual(to, back.data_type)
5953

60-
def test_fused_matmul(self):
61-
model = make_model(
62-
make_graph(
63-
[make_node("FusedMatMul", ["X", "Y"], ["Z"], domain="com.microsoft")],
64-
"name",
65-
[
66-
make_tensor_value_info("X", TensorProto.FLOAT, None),
67-
make_tensor_value_info("Y", TensorProto.FLOAT, None),
68-
],
69-
[make_tensor_value_info("Z", TensorProto.FLOAT, None)],
70-
),
71-
opset_imports=[make_opsetid("", 18), make_opsetid("com.microsoft", 1)],
72-
)
73-
ref = ExtendedReferenceEvaluator(model)
74-
a = np.arange(4).reshape(-1, 2)
75-
got = ref.run(None, {"X": a, "Y": a})
76-
self.assertEqualArray(a @ a, got[0])
77-
7854

7955
if __name__ == "__main__":
8056
unittest.main(verbosity=2)

‎_unittests/ut_reference/test_reference_ops.py

Lines changed: 64 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,64 @@
1+
import unittest
2+
import numpy as np
3+
from onnx import TensorProto
4+
from onnx.helper import (
5+
make_graph,
6+
make_model,
7+
make_node,
8+
make_tensor_value_info,
9+
make_opsetid,
10+
)
11+
from onnx_array_api.ext_test_case import ExtTestCase
12+
from onnx_array_api.reference import ExtendedReferenceEvaluator
13+
14+
15+
class TestReferenceOps(ExtTestCase):
16+
17+
def test_fused_matmul(self):
18+
model = make_model(
19+
make_graph(
20+
[make_node("FusedMatMul", ["X", "Y"], ["Z"], domain="com.microsoft")],
21+
"name",
22+
[
23+
make_tensor_value_info("X", TensorProto.FLOAT, None),
24+
make_tensor_value_info("Y", TensorProto.FLOAT, None),
25+
],
26+
[make_tensor_value_info("Z", TensorProto.FLOAT, None)],
27+
),
28+
opset_imports=[make_opsetid("", 18), make_opsetid("com.microsoft", 1)],
29+
)
30+
ref = ExtendedReferenceEvaluator(model)
31+
a = np.arange(4).reshape(-1, 2)
32+
got = ref.run(None, {"X": a, "Y": a})
33+
self.assertEqualArray(a @ a, got[0])
34+
35+
def test_fused_matmul11(self):
36+
model = make_model(
37+
make_graph(
38+
[
39+
make_node(
40+
"FusedMatMul",
41+
["X", "Y"],
42+
["Z"],
43+
transA=1,
44+
transB=1,
45+
domain="com.microsoft",
46+
)
47+
],
48+
"name",
49+
[
50+
make_tensor_value_info("X", TensorProto.FLOAT, None),
51+
make_tensor_value_info("Y", TensorProto.FLOAT, None),
52+
],
53+
[make_tensor_value_info("Z", TensorProto.FLOAT, None)],
54+
),
55+
opset_imports=[make_opsetid("", 18), make_opsetid("com.microsoft", 1)],
56+
)
57+
ref = ExtendedReferenceEvaluator(model)
58+
a = np.arange(4).reshape(-1, 2)
59+
got = ref.run(None, {"X": a, "Y": a})
60+
self.assertEqualArray(a.T @ a.T, got[0])
61+
62+
63+
if __name__ == "__main__":
64+
unittest.main(verbosity=2)

‎onnx_array_api/reference/evaluator.py

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -110,4 +110,7 @@ def run(self, *args, **kwargs):
110110
"""
111111
See :meth:`onnx.reference.ReferenceEvaluator.run`.
112112
"""
113+
if len(args) == 1 and isinstance(args[0], list):
114+
feeds = dict(zip(self.input_names, args[0]))
115+
return self.run(None, feeds, **kwargs)
113116
return ReferenceEvaluator.run(self, *args, **kwargs)

‎onnx_array_api/reference/evaluator_yield.py

Lines changed: 23 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,5 @@
11
from dataclasses import dataclass
2-
from typing import Any, Dict, List, Iterator, Optional, Tuple
2+
from typing import Any, Dict, List, Iterator, Optional, Tuple, Union
33
from enum import IntEnum
44
import numpy as np
55
from onnx import ModelProto, TensorProto, ValueInfoProto
@@ -77,6 +77,12 @@ def make_summary(value: Any, length: int = 4, modulo: int = 26) -> str:
7777
:param module: discretization parameter
7878
:return: short string
7979
"""
80+
if isinstance(value, np.float32):
81+
# This should not happen.
82+
value = np.array(value)
83+
assert isinstance(
84+
value, np.ndarray
85+
), f"Unexpected type {type(value)} for value, it must be a numpy array."
8086
value4 = np.zeros(length, dtype=np.float64)
8187
if value.size <= length:
8288
value4[: value.size] = value.flatten().astype(np.float64)
@@ -170,6 +176,9 @@ def enumerate_results(
170176
outputs = node.run(*inputs, **linked_attributes)
171177
except Exception:
172178
if raise_exc:
179+
# ExtendedReferenceEvaluator(self.onnx_model, verbose=10).run(
180+
# None, feed_inputs
181+
# )
173182
raise
174183
yield_output = False
175184
break
@@ -286,12 +295,12 @@ def distance_sequence(
286295
:param s2: second sequence
287296
:return: distance and alignment
288297
"""
289-
delay = self.max_lag
298+
delay = max(self.max_lag, abs(len(s2) - len(s1)) + 1)
290299
distance = {(-1, -1): 0}
291300
predecessor = {(-1, -1): None}
292301
for i in range(len(s1)):
293302
for j in range(max(0, i - delay), min(len(s2), i + delay)):
294-
best = 1e100
303+
best = distance.get((i, j), 1e100)
295304
pred = None
296305
ki, kj = i - 1, j - 1
297306
if (ki, kj) in distance:
@@ -418,7 +427,7 @@ def generate_inputs(model: ModelProto) -> List[np.ndarray]:
418427
def compare_onnx_execution(
419428
model1: ModelProto,
420429
model2: ModelProto,
421-
inputs: Optional[List[Any]] = None,
430+
inputs: Optional[Union[List[Any], Tuple[Dict[str, Any]]]] = None,
422431
verbose: int = 0,
423432
raise_exc: bool = True,
424433
) -> Tuple[List[ResultExecution], List[ResultExecution], List[Tuple[int, int]]]:
@@ -430,7 +439,8 @@ def compare_onnx_execution(
430439
431440
:param model1: first model
432441
:param model2: second model
433-
:param inputs: inputs to use
442+
:param inputs: inputs to use, a list of inputs if both models have
443+
the same number of inputs or two dictionaries, one for each model
434444
:param verbose: verbosity
435445
:param raise_exc: raise exception if the execution fails or stop at the error
436446
:return: four results, a sequence of results for the first model and the second model,
@@ -440,8 +450,14 @@ def compare_onnx_execution(
440450
print("[compare_onnx_execution] generate inputs")
441451
if inputs is None:
442452
inputs = generate_inputs(model1)
443-
feeds1 = {i.name: v for i, v in zip(model1.graph.input, inputs)}
444-
feeds2 = {i.name: v for i, v in zip(model2.graph.input, inputs)}
453+
if isinstance(inputs, tuple):
454+
assert len(inputs) == 2, f"Unexpected number {len(inputs)} of inputs."
455+
feeds1, feeds2 = inputs
456+
else:
457+
feeds1 = {i.name: v for i, v in zip(model1.graph.input, inputs)}
458+
feeds2 = {i.name: v for i, v in zip(model2.graph.input, inputs)}
459+
assert isinstance(feeds1, dict), f"Unexpected type {type(feeds1)} for inputs"
460+
assert isinstance(feeds2, dict), f"Unexpected type {type(feeds2)} for inputs"
445461
if verbose:
446462
print(f"[compare_onnx_execution] got {len(inputs)} inputs")
447463
print("[compare_onnx_execution] execute first model")

‎onnx_array_api/reference/ops/op_fused_matmul.py

Lines changed: 9 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -22,10 +22,14 @@ def _run(
2222
transBatchB == 0
2323
), f"Not implemented for transBatchB==1 and {A.shape}x{B.shape}"
2424
if transA:
25-
dim = len(A.shape)
26-
A = A.transpose(axes=(dim - 2, dim - 1))
25+
perm = list(range(len(A.shape)))
26+
dim = len(perm)
27+
perm[dim - 2], perm[dim - 1] = perm[dim - 1], perm[dim - 2]
28+
A = np.transpose(A, perm)
2729
if transB:
28-
dim = len(B.shape)
29-
B = B.transpose(axes=(dim - 2, dim - 1))
30+
perm = list(range(len(B.shape)))
31+
dim = len(perm)
32+
perm[dim - 2], perm[dim - 1] = perm[dim - 1], perm[dim - 2]
33+
B = np.transpose(B, perm)
3034
a = np.array(alpha, dtype=A.dtype)
31-
return (A @ B * a,)
35+
return (np.matmul(A, B) * a,)

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