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sgbihuMayureshV1
sgbihu
and
MayureshV1
authored
CVS-174886: Make onnxruntime tests pass on OpenVINO (#790)
* Change the checker to support threshold for uint16/uint4/int4 * Disable loop case for OV-EP due to floating nodes * Disable the case for OV-EP due to input conflict * Add threshold for uint8/int8/uint16 cases * Fix build warning * New changes after rebase * Change code based on review --------- Co-authored-by: MayureshV1 <[email protected]>
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8 files changed

+92
-7
lines changed

8 files changed

+92
-7
lines changed

‎onnxruntime/test/contrib_ops/quantize_ops_test.cc‎

Lines changed: 2 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -287,6 +287,7 @@ TEST(QuantizeLinearContribOpTest, QuantizeLinear_per_tensor_float_int8) {
287287
127, -127,
288288
127, -128,
289289
127, -128});
290+
test.SetOutputAbsErr("y", 1.0f);
290291
// Disable Tensorrt EP due to error: node1_quantize_scale_node: out of bounds channel axis 1. Number of input dimensions is 1.
291292
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
292293
}
@@ -311,6 +312,7 @@ TEST(QuantizeLinearContribOpTest, QuantizeLinear_per_tensor_float_uint16) {
311312
32769, 32765,
312313
65535, 0,
313314
65535, 0});
315+
test.SetOutputAbsErr("y", 1.0f);
314316

315317
// Disable Tensorrt EP due to error: unsupported data type
316318
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});

‎onnxruntime/test/providers/cpu/controlflow/loop_test.cc‎

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -828,7 +828,8 @@ TEST(Loop, Opset11WithNoVariadicInputsAndOutputs) {
828828
test.AddOutput<float>("loop_scan_out", {1}, {1.0f});
829829

830830
// Disable TensorRT on unsupported data type BOOL
831-
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
831+
// Disable OpenVino for floating nodes
832+
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider});
832833
}
833834

834835
// Test a combination of things:

‎onnxruntime/test/providers/cpu/tensor/cast_op_test.cc‎

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -853,6 +853,9 @@ TEST(CastOpTest, Int32ToInt4x2OddNumberOfElements) {
853853
}
854854

855855
TEST(CastOpTest, Int32ToInt4x2EmptyTensor) {
856+
if (DefaultOpenVINOExecutionProvider().get() != nullptr) {
857+
GTEST_SKIP() << "The OpenVINO not support 0 size input";
858+
}
856859
// GIVEN
857860
const std::vector<int64_t> empty_shape{0};
858861
const std::vector<int32_t> empty_input = {};

‎onnxruntime/test/providers/cpu/tensor/concat_op_test.cc‎

Lines changed: 2 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -73,6 +73,7 @@ TEST(ConcatOpTest, Concat1D_2) {
7373
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
7474
{kTensorrtExecutionProvider, // TensorRT: no support for dynamic shape tensor
7575
kNnapiExecutionProvider, // NNAPI: concat does not support 0 size input
76+
kOpenVINOExecutionProvider, // OpenVINO: does not support 0 size input
7677
kQnnExecutionProvider}); // QNN: not support dynamic shape tensor
7778
}
7879

@@ -118,6 +119,7 @@ TEST(ConcatOpTest, Concat2D_3) {
118119
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
119120
{kTensorrtExecutionProvider, // TensorRT: no support for dynamic shape tensor
120121
kNnapiExecutionProvider, // NNAPI: concat does not support 0 size input
122+
kOpenVINOExecutionProvider, // OpenVINO: does not support 0 size input
121123
kQnnExecutionProvider}); // QNN: not support dynamic shape tensor
122124
}
123125

‎onnxruntime/test/providers/cpu/tensor/quantize_linear_test.cc‎

Lines changed: 7 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -448,6 +448,7 @@ TEST(QuantizeLinearOpTest, Uint16) {
448448
32769, 32765,
449449
65535, 0,
450450
65535, 0});
451+
test.SetOutputAbsErr("y", 1.0f);
451452

452453
// Disable Tensorrt EP due to error: unsupported data type
453454
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
@@ -477,6 +478,7 @@ TEST(QuantizeLinearOpTest, Int16) {
477478
32767, -32768,
478479
32767, -32768,
479480
32767, -32768});
481+
test.SetOutputAbsErr("y", 1.0f);
480482

481483
// Disable Tensorrt EP due to error: unsupported data type
482484
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
@@ -501,6 +503,7 @@ TEST(QuantizeLinearOpTest, Int4) {
501503
test.AddOutput<Int4x2>("y", dims,
502504
{Int4x2(-8, -7), Int4x2(-1, 1), Int4x2(2, 7),
503505
Int4x2(7, unused_val)});
506+
test.SetOutputAbsErr("y", 1.0f);
504507

505508
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
506509
}
@@ -568,6 +571,7 @@ TEST(QuantizeLinearOpTest, OddLarge_Int4) {
568571
test.AddInput<float>("scale", {}, {scale}, true);
569572
test.AddInput<Int4x2>("zero_point", {}, {Int4x2(zp, unused_val)}, true);
570573
test.AddOutput<Int4x2>("y", dims, output);
574+
test.SetOutputAbsErr("y", 1.0f);
571575

572576
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
573577
}
@@ -594,6 +598,7 @@ TEST(QuantizeLinearOpTest, OddLarge_UInt4) {
594598
test.AddInput<float>("scale", {}, {scale}, true);
595599
test.AddInput<UInt4x2>("zero_point", {}, {UInt4x2(zp, unused_val)}, true);
596600
test.AddOutput<UInt4x2>("y", dims, output);
601+
test.SetOutputAbsErr("y", 1.0f);
597602

598603
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
599604
}
@@ -611,6 +616,7 @@ TEST(QuantizeLinearOpTest, Int8_NegativeZeroPoint) {
611616
test.AddInput<float>("y_scale", {}, {.039215686f});
612617
test.AddInput<int8_t>("y_zero_point", {}, {-23});
613618
test.AddOutput<int8_t>("y", dims, {-23, 28, 53, 104, 127, -74, -128, -128});
619+
test.SetOutputAbsErr("y", 1.0f);
614620
// Disable Tensorrt EP due to the error, node1_quantize_scale_node: out of bounds channel axis 1. Number of input dimensions is 1.
615621
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
616622
}
@@ -628,6 +634,7 @@ TEST(QuantizeLinearOpTest, Int8_PositiveZeroPoint) {
628634
test.AddInput<float>("y_scale", {}, {.039215686f});
629635
test.AddInput<int8_t>("y_zero_point", {}, {23});
630636
test.AddOutput<int8_t>("y", dims, {23, 74, 99, 127, 127, -28, -104, -128});
637+
test.SetOutputAbsErr("y", 1.0f);
631638
// Disable Tensorrt EP due to error:node1_quantize_scale_node: out of bounds channel axis 1. Number of input dimensions is 1.
632639
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
633640
}

‎onnxruntime/test/providers/cpu/tensor/resize_op_test.cc‎

Lines changed: 2 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -308,6 +308,7 @@ TEST(ResizeOpTest, NhwcResizeOpLinearDownSampleTest_4DBilinear_uint8) {
308308
std::vector<uint8_t> Y = {2, 4};
309309

310310
test.AddOutput<uint8_t>("Y", {N, static_cast<int64_t>(H * scales[1]), static_cast<int64_t>(W * scales[2]), C}, Y);
311+
test.SetOutputAbsErr("Y", 1.0f);
311312
// CUDA: result mismatch due to not implementing NHWC support
312313
// ROCm: results mismatch
313314
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
@@ -647,6 +648,7 @@ TEST(ResizeOpTest, NhwcResizeOpLinearDownSampleTest_4DBilinear_pytorch_half_pixe
647648
std::vector<uint8_t> Y = {1, 7, 12};
648649

649650
test.AddOutput<uint8_t>("Y", {N, sizes[1], sizes[2], C}, Y);
651+
test.SetOutputAbsErr("Y", 1.0f);
650652
// CUDA: result mismatch due to not implementing NHWC support
651653
// ROCm: results mismatch
652654
// DML: results mismatch

‎onnxruntime/test/providers/cpu/tensor/slice_op.test.cc‎

Lines changed: 4 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -540,6 +540,10 @@ TEST(SliceTest, Slice1D_ReverseAllAxes_1) {
540540
GTEST_SKIP() << "Skipping because of the following error: Expected output shape [{4}] did not match run output shape [{0}] for output";
541541
}
542542

543+
if (DefaultOpenVINOExecutionProvider().get() != nullptr) {
544+
GTEST_SKIP() << "Skipping because of the following error: The input ends do not support int max when step is negative.";
545+
}
546+
543547
RunSliceTest<float>({4},
544548
{1.0f, 2.0f, 3.0f, 4.0f},
545549
{-1},

‎onnxruntime/test/unittest_util/checkers.cc‎

Lines changed: 70 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -225,17 +225,27 @@ template <>
225225
struct TensorCheck<Int4x2> {
226226
void operator()(const Tensor& expected, const Tensor& actual, const ValidateOutputParams& params,
227227
const std::string& /*provider_type*/) const {
228-
ORT_UNUSED_PARAMETER(params);
228+
const bool has_abs_err = params.absolute_error.has_value();
229+
Tensor expected_sorted, actual_sorted;
229230
const Int4x2* cur_expected;
230231
const Int4x2* cur_actual;
231232
const auto size = narrow<size_t>(actual.Shape().Size());
232233
cur_expected = expected.Data<Int4x2>();
233234
cur_actual = actual.Data<Int4x2>();
235+
double threshold = 0.0f;
236+
if (has_abs_err) {
237+
threshold = *(params.absolute_error);
238+
}
234239

235240
for (size_t i = 0; i < size; ++i) {
236241
size_t r = i >> 1;
237242
size_t c = i & 0x1;
238-
EXPECT_EQ(cur_expected[r].GetElem(c), cur_actual[r].GetElem(c)) << "i:" << i;
243+
// TODO: the relative error is not used for int4 yet.
244+
if (has_abs_err) {
245+
EXPECT_NEAR(cur_expected[r].GetElem(c), cur_actual[r].GetElem(c), threshold) << "i:" << i;
246+
} else {
247+
EXPECT_EQ(cur_expected[r].GetElem(c), cur_actual[r].GetElem(c)) << "i:" << i;
248+
}
239249
}
240250
}
241251
};
@@ -244,17 +254,28 @@ template <>
244254
struct TensorCheck<UInt4x2> {
245255
void operator()(const Tensor& expected, const Tensor& actual, const ValidateOutputParams& params,
246256
const std::string& /*provider_type*/) const {
247-
ORT_UNUSED_PARAMETER(params);
257+
const bool has_abs_err = params.absolute_error.has_value();
258+
Tensor expected_sorted, actual_sorted;
248259
const UInt4x2* cur_expected;
249260
const UInt4x2* cur_actual;
250261
const auto size = narrow<size_t>(actual.Shape().Size());
251262
cur_expected = expected.Data<UInt4x2>();
252263
cur_actual = actual.Data<UInt4x2>();
253264

254-
for (size_t i = 0; i < size; ++i) {
265+
double threshold = 0.0f;
266+
if (has_abs_err) {
267+
threshold = *(params.absolute_error);
268+
}
269+
270+
for (size_t i = 0; i < static_cast<size_t>(size); ++i) {
255271
size_t r = i >> 1;
256272
size_t c = i & 0x1;
257-
EXPECT_EQ(cur_expected[r].GetElem(c), cur_actual[r].GetElem(c)) << "i:" << i;
273+
// TODO: the relative error is not used for int4 yet.
274+
if (has_abs_err) {
275+
EXPECT_NEAR(cur_expected[r].GetElem(c), cur_actual[r].GetElem(c), threshold) << "i:" << i;
276+
} else {
277+
EXPECT_EQ(cur_expected[r].GetElem(c), cur_actual[r].GetElem(c)) << "i:" << i;
278+
}
258279
}
259280
}
260281
};
@@ -292,7 +313,7 @@ struct TensorCheck<uint8_t> {
292313
// For any other EPs, we still expect an exact match for the results
293314
// TODO: Verify if DML can possibly have a ROUNDING_MODE parameter and conform to the other EPs #41968513
294315
if ((provider_type == kNnapiExecutionProvider || provider_type == kDmlExecutionProvider ||
295-
provider_type == kXnnpackExecutionProvider) &&
316+
provider_type == kXnnpackExecutionProvider || provider_type == kOpenVINOExecutionProvider) &&
296317
(has_abs_err || has_rel_err)) {
297318
double threshold = has_abs_err ? *(params.absolute_error)
298319
: 0.0;
@@ -357,6 +378,49 @@ struct TensorCheck<int8_t> {
357378
}
358379
};
359380

381+
template <>
382+
struct TensorCheck<uint16_t> {
383+
void operator()(const Tensor& expected,
384+
const Tensor& actual,
385+
const ValidateOutputParams& params,
386+
const std::string& ) const {
387+
const bool has_abs_err = params.absolute_error.has_value();
388+
const bool has_rel_err = params.relative_error.has_value();
389+
390+
Tensor expected_sorted, actual_sorted;
391+
const uint16_t* cur_expected;
392+
const uint16_t* cur_actual;
393+
const auto size = actual.Shape().Size();
394+
if (params.sort_output) {
395+
sort_expected_and_actual_buffers<uint16_t>(expected, expected_sorted, actual, actual_sorted);
396+
cur_expected = expected_sorted.Data<uint16_t>();
397+
cur_actual = actual_sorted.Data<uint16_t>();
398+
} else {
399+
cur_expected = expected.Data<uint16_t>();
400+
cur_actual = actual.Data<uint16_t>();
401+
}
402+
403+
if (has_abs_err || has_rel_err) {
404+
double threshold = has_abs_err ? *(params.absolute_error)
405+
: 0.0;
406+
407+
for (int64_t i = 0; i < size; ++i) {
408+
if (has_rel_err) {
409+
EXPECT_NEAR(cur_expected[i], cur_actual[i],
410+
*(params.relative_error) * cur_expected[i]) // expected[i] is unsigned, can't be negative
411+
<< "i:" << i;
412+
} else { // has_abs_err
413+
EXPECT_NEAR(cur_expected[i], cur_actual[i], threshold) << "i:" << i;
414+
}
415+
}
416+
} else {
417+
for (int64_t i = 0; i < size; ++i) {
418+
EXPECT_EQ(cur_expected[i], cur_actual[i]) << "i:" << i;
419+
}
420+
}
421+
}
422+
};
423+
360424
template <>
361425
struct TensorCheck<double> {
362426
void operator()(const Tensor& expected,

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