/builds/devtechproviz/dl/ort-builder/onnxruntime/onnxruntime/python/onnxruntime_pybind_state.cc:388:14:
error: missing initializer for member
'OrtTensorRTProviderOptionsV2::trt_cuda_graph_enable'
[-Werror=missing-field-initializers]
388 | 0};
|
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Current TRT EP can support model which has nested control flow ops
(multiple level subgraphs). But it fails at a case where the subgraph
has outer scope value that is defined several levels up in the top-level
graph, in this case, the outer scope value is the input of the top-level
graph. The outer scope values are not properly handled during TRT EP's
subgraph reconstruction stage and fails at `graph.resolve()`.
The way ORT gets capability from EPs is a bottom-up approach meaning
inner most subgraph gets handled first. TRT EP reconstructs each
subgraph level by level and following modifications are made to fix the
outer scope values issue:
- `SetGraphOuterScopeValuesAndInputs()` and `SetAllGraphInputs()` are
added to handle outer scope values and add those values as graph inputs
if needed in order to make `graph.resolve()` happy.
- Change to use `GetNodeArgIncludingParentGraphs` so that when creating
the fused TRT node for some subgraphs in`
Graph::CreateFusedSubGraphNode()`, it can get the NodeArgs for outer
scope values from top-level graph.
This PR fixes https://github.com/microsoft/onnxruntime/issues/16217
### Disable large index tests due to limited GPU mem
Recently following two tests fail due to GPU mem not enough, not sure
what else program running using GPU as well. So disable them for now to
unblock the required CI.
```
1: [ FAILED ] 2 tests, listed below:
1: [ FAILED ] CrossEntropyTest.SoftmaxCrossEntropyLossInternal_LargeSizeTensorUInt64Index
1: [ FAILED ] CrossEntropyTest.SoftmaxCrossEntropyLossInternalGrad_LargeSizeTensorUInt64Index
2023-07-23T02:15:39.7559251Z 1: [ RUN ] CrossEntropyTest.SoftmaxCrossEntropyLossInternal_LargeSizeTensorUInt64Index
2023-07-23T02:16:53.0904576Z 1: 2023-07-23 02:16:53.089586592 [E:onnxruntime:SoftmaxCrossEntropyLossInternal, sequential_executor.cc:514 ExecuteKernel] Non-zero status code returned while running SoftmaxCrossEntropyLossInternal node. Name:'node1' Status Message: /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc:376 void* **onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn) Failed to allocate memory for requested buffer of size 4294973440**
2023-07-23T02:16:53.0905775Z 1:
2023-07-23T02:16:53.0906087Z 1: /onnxruntime_src/onnxruntime/test/providers/base_tester.cc:323: Failure
2023-07-23T02:16:53.0906698Z 1: Expected equality of these values:
2023-07-23T02:16:53.0907086Z 1: expect_result
2023-07-23T02:16:53.0907564Z 1: Which is: 4-byte object <00-00 00-00>
2023-07-23T02:16:53.0973055Z 1: ExpectResult::kExpectFailure
2023-07-23T02:16:53.0973984Z 1: Which is: 4-byte object <01-00 00-00>
2023-07-23T02:16:53.0975375Z 1: Run failed but expected success: Non-zero status code returned while running SoftmaxCrossEntropyLossInternal node. Name:'node1' Status Message: /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc:376 void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn) Failed to allocate memory for requested buffer of size 4294973440
2023-07-23T02:16:53.0976198Z 1:
2023-07-23T02:16:53.0976483Z 1: Google Test trace:
2023-07-23T02:16:53.0976818Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.0977229Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.0977639Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 2345
2023-07-23T02:16:53.0978035Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 5678
2023-07-23T02:16:53.0978441Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 1234
2023-07-23T02:16:53.1303810Z 1: /onnxruntime_src/orttraining/orttraining/test/training_ops/cuda/cross_entropy_test.cc:443: Failure
2023-07-23T02:16:53.1304644Z 1: Expected equality of these values:
2023-07-23T02:16:53.1304974Z 1: ret.first
2023-07-23T02:16:53.1305685Z 1: Which is: 4-byte object <04-00 00-00>
2023-07-23T02:16:53.1306030Z 1: COMPARE_RESULT::SUCCESS
2023-07-23T02:16:53.1306414Z 1: Which is: 4-byte object <00-00 00-00>
2023-07-23T02:16:53.1306754Z 1: Unsupported compare with CompareOrtValueNumerals.
2023-07-23T02:16:53.1307487Z 1: Google Test trace:
2023-07-23T02:16:53.1307848Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.1308252Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.1308652Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 2345
2023-07-23T02:16:53.1309068Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 5678
2023-07-23T02:16:53.1309460Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 1234
2023-07-23T02:16:53.1309889Z 1: /onnxruntime_src/orttraining/orttraining/test/training_ops/cuda/cross_entropy_test.cc:443: Failure
2023-07-23T02:16:53.1310239Z 1: Expected equality of these values:
2023-07-23T02:16:53.1310527Z 1: ret.first
2023-07-23T02:16:53.1310893Z 1: Which is: 4-byte object <04-00 00-00>
2023-07-23T02:16:53.1311208Z 1: COMPARE_RESULT::SUCCESS
2023-07-23T02:16:53.1311600Z 1: Which is: 4-byte object <00-00 00-00>
2023-07-23T02:16:53.1311921Z 1: Unsupported compare with CompareOrtValueNumerals.
2023-07-23T02:16:53.1312229Z 1: Google Test trace:
2023-07-23T02:16:53.1312556Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.1312951Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 8910
2023-07-23T02:16:53.1313362Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 2345
2023-07-23T02:16:53.1313749Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 5678
2023-07-23T02:16:53.1314156Z 1: /onnxruntime_src/onnxruntime/test/common/random_generator.h:49: ORT test random seed: 1234
2023-07-23T02:16:53.4476437Z 1: [ FAILED ] CrossEntropyTest.SoftmaxCrossEntropyLossInternal_LargeSizeTensorUInt64Index (73692 ms)
```
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
1. use the pool with VS2022
2. upgrade System.Memory to 4.5.5
### Motivation and Context
Solve the build error while using VS2022:
`[Failure] Msbuild failed when processing the file
'D:\a\_work\1\s\csharp\src\Microsoft.ML.OnnxRuntime\Microsoft.ML.OnnxRuntime.csproj'
with message: Method not found: 'System.ReadOnlySpan`1<Char>
Microsoft.IO.Path.GetFileName(System.ReadOnlySpan`1<Char>)'`
Ref:
https://stackoverflow.com/questions/73399777/azure-build-failing-due-to-method-not-found-system-readonlyspan1char-micros
### Description
Changes allow downloading prebuilt protoc compiler when building
WebAssebly version on mac systems.
Otherwise it tries to build a js/wasm version of protoc and throws an
error while executing it: "protoc.js permission denied"
### Motivation and Context
I need to switch between my main working computer and a PC to make
changes to WebAssebly build. Would like not to do that anymore.
### Description
<!-- Describe your changes. -->
Allocating new GPUBuffer in every session.run is not efficient. We
should make it only happen in the first run. In the following runs, we
should try to reuse those buffers.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
- This PR is for performance.
See mobilenetv2 becomes 9.58 ms from 12.9 ms.
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at
bottom):
* __->__ #16789
Bump ruff to 0.0.278 and fix new lint errors. I added noqa to all
existing RUF012 errors which requires mutable class variables to be
annotated with `ClassVar`, as well as all PERF issues.
Signed-off-by: Justin Chu <justinchu@microsoft.com>
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at
bottom):
* #16789
* __->__ #16788
This change fixes the N802 lint errors by renaming the test case to use
snake case.
### Description
The Java API currently only supports fp16 output tensors which it
automatically casts to floats on the way out. This PR adds support for
creating fp16 and bf16 tensors (from `java.nio.Buffer` objects or as the
output of models, creation from Java short arrays is not supported),
along with efficient methods for casting `FloatBuffer` into
`ShortBuffer` filled with fp16 or bf16 values and vice versa.
The fp16 conversions use a trick to pull in the efficient conversion
methods added to Java 20, falling back to ports of the MLAS methods
otherwise. The Java 20 methods can be special cased by the C2 JIT
compiler to emit the single instruction on x86 and ARM which converts
fp32<->fp16, or the vectorized versions thereof, so they should be quite
a bit faster than the MLAS ported one.
### Motivation and Context
fp16 and bf16 are increasingly popular formats and we've had several
requests for this functionality. Fixes#7003.
cc @yuslepukhin @cassiebreviu
---------
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
### Description
1) Added Sequence And Maps convenience APIs to create input Sequences
and Maps
and also visit the outputs.
2) Address OrtValue design issue when the values are created on top of
the
managed memory and the ortValues are used for sequence and maps
creation.
We should retain the original managed instances that keep the memory
pinned.
We opt to keep track of those and dispose of them within an instance of
OrtValue
that represents a Map or a Sequence.
3) Set `LangVersion` to default per [MS Versioning
Docs.](https://learn.microsoft.com/en-us/dotnet/csharp/language-reference/configure-language-version)
### Motivation and Context
1) When writing code examples, use of Map and Sequences API proved to be
cumbersome.
2) It is a BUG, that we should address, as the managed memory can move
by the GC and lead to
intermittent crashes.
3) Make use of the most feature of the C#.
### Description
Add op support for LayerNorm, Asin, Sign.
Enable QDQ node unit support for Sin Op
---------
Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com>
### Description
torch.norm is deprecated as mentioned in issue #16751. This PR replaces
the call to torch.norm by the options suggested by torch documentation.
### Description
A [previous PR](https://github.com/microsoft/onnxruntime/pull/16531)
added a temporary directory to save the model optimizations after
loading a model into an `InferenceSession`. Many models that have an
external data file, however, require the data file to be in the same
directory as the ONNX model file. Because the model is saved in a
temporary directory and the data is saved in another directory, this
causes a `FileNotFoundError` error when trying to load the model in the
temporary directory.
This PR fixes this error by saving the external data file in the same
directory that the optimized model is located in.
### Motivation and Context
This PR fixes a bug with using a temporary directory while running the
optimizer for models that have an external data file.
This pull request contains a few changes:
1. Adds support for string ort values.
2. Fixes the training minimal build (that was broken with #16601) by
putting custom op registration behind #ifdefs
3. Fixes the iOS pod package generation (that was again broken with
#16601) by explicitly providing paths to be copied during pod creation.
### Description
- Updates the default QNN SDK to 2.12 for CI pipelines
- Adds a disabled InstanceNormalization test for regression on QNN SDK
2.12
- Cleans up logs for unsupported ops.
### Motivation and Context
Test with the latest QNN SDK.
Otherwise, an unsupported version of gtest/gmock will be found at
/opt/conda/include for ROCm builds. Though this issue was initially
found for ROCm builds, the issue is generic. onnxruntime requires a
specific version of googletest and should not rely on locating
googletest using find_package.
The ROCm error was:
```
In file included from /opt/conda/include/gmock/gmock-spec-builders.h:75,
from /opt/conda/include/gmock/gmock-generated-function-mockers.h:47,
from /opt/conda/include/gmock/gmock-function-mocker.h:39,
from /opt/conda/include/gmock/gmock.h:61,
from /stage/onnxruntime/onnxruntime/test/util/test_utils.cc:17:
/opt/conda/include/gmock/gmock-matchers.h: In instantiation of ‘bool testing::internal::PointwiseMatcher<TupleMatcher, RhsContainer>::Impl<LhsContainer>::
MatchAndExplain(LhsContainer, testing::MatchResultListener*) const [with LhsContainer = const gsl::span<const float>&; TupleMatcher = testing::internal::
FloatingEq2Matcher<float>; RhsContainer = gsl::span<const float>]’:
/opt/conda/include/gmock/gmock-matchers.h:2303:10: required from here
/opt/conda/include/gmock/gmock-matchers.h:2312:48: error: no type named ‘const_iterator’ in ‘testing::internal::PointwiseMatcher<testing::internal::
FloatingEq2Matcher<float>, gsl::span<const float> >::Impl<const gsl::span<const float>&>::LhsStlContainer’ {aka ‘class gsl::span<const float>’}
```
### Description
<!-- Describe your changes. -->
Support Op Pad for WebNN EP. It aims to support three modes (constant,
reflect and edge). For now, only constant can be tested with Chrome
Canary.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Support more models like SD1.5-VAE-encode.
### Description
<!-- Describe your changes. -->
Comment out ORT-Nightly feed in NuGet.config to see if that makes the
Secure Supply Chain Analysis CI step happy.
Add info to readme on manually adding feed and using it.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
This PR is includes changes in the documentation of _readmeOV.rst_ file
and also the changes in the dockerfile which enables to build ORT with
latest OpenVINO 2023.0.0
### Motivation and Context
Modified the dockerfile to incorporate the latest version of OpenVINO
(2023.0.0) for building Onnxruntime.
The changes in the PR aim to improve the overall user experience by
providing accurate and up-to-date documentation while leveraging latest
OpenVINO 2023.0.0
### Description
This change upgrades a lot of dependencies. There are 2 motivations of
doing this change:
- fix the security issue reported by dependabot (protobufjs Prototype
Pollution vulnerability -
https://github.com/advisories/GHSA-h755-8qp9-cq85)
- resolve the requirement of using ONNX IR_VERSION 9 (#16638)
This requires:
- upgrade protobufjs to v7.2.4
- upgrade library 'onnx-proto' to consume latest ONNX release (v1.14.0).
Problems:
- protobufjs v7.2.4 depends on long.js v5, which does not work well with
typescript (commonjs).
- onnx-proto depends on this fix with a new release of long.js
- long.js is in maintenance and it takes longer than expected to put in
new changes
Solutions:
- use a patch script in `preprepare` to copy type declarations to make
long.js work with typescript (commonjs)
- generate onnx protobuf JS/TS files and put them under
js/web/lib/onnxjs/ort-schema/protobuf folder - remove 'onnx-proto' from
dependency.
- apply fixes to generated onnx.d.ts
### Description
- Fixes support for ArgMin/ArgMax to QNN CPU and HTP backends.
- Adds Q/DQ node unit selection logic.
- Handles casting int64 output to uint32 when necessary.
- Adds unit tests for ArgMax/ArgMin.
### Motivation and Context
QNN EP did not actually support ArgMin/ArgMax. Unit tests revealed that
the existing translation was not sufficient to support these ops.
### Description
Fix some issues found in GPT-NeoX graph fusion:
(1) GPT-NeoX uses float16 weights. The step of using onnxruntime with
opt_level==1 uses CPU provider. Since most operators does not have fp16
in CPU EP, so extra Cast nodes are added to up cast to fp32.
(2) Add is shared by two LayerNormalization children, and
SkipLayerNormalization might cause invalid graph.
(3) Reshape fusion might miss since some part only check initializer but
not Constant.
This PR adds a check whether model uses FP16, and output a warning when
use_gpu is not True, and use GPU provider for graph optimization when use_gpu=True.
There are several global configs used by DORT.
```py
DEFAULT_ONNX_EXPORTER_OPTIONS = torch.onnx._internal.exporter.ResolvedExportOptions(
torch.onnx._internal.exporter.ExportOptions()
)
# TODO(wechi): This line must generate result identical to the call of
# _create_onnx_supports_op_overload_table(...) inside
# create_onnx_friendly_decomposition_table(...) in
# torch/onnx/_internal/fx/decomposition_table.py.
_SUPPORT_DICT = torch.onnx._internal.fx.decomposition_table._create_onnx_supports_op_overload_table(
DEFAULT_ONNX_EXPORTER_OPTIONS.onnx_registry
) # type: ignore
_EXTRA_SUPPORT_DICT: Dict[str, Any] = {
"getattr": None,
"_operator.getitem": None,
}
DORT_DECOMPOSITION_TABLE = DEFAULT_ONNX_EXPORTER_OPTIONS.decomposition_table
```
We can see all but `_EXTRA_SUPPORT_DICT` are extracted from deduced from
ONNX exporter's options. As there are many ways to configure ONNX
exporter's options, we decided to move these variables to `OrtBackend`'s
`__init__` so that the construction of `OrtBackend` becomes more
flexible (especially for enabling dynamic shape or not).
GemmSoftmaxGemmTunble occasionally broken with large numerical error.
The root cause of this error is CK's Strided Batched Gemm has larger
error under a specific initialization distribution
`(multinormal_distribution)`.
Generic(Gemm1 + Softmax + Gemm2) implementation is one instance of
GemmSoftmaxGemmTunble. Gemm1 and Gemm2 in Generic implementation are
TunableOps when tuning enabled. In some case GemmSoftmaxGemmTunble
select Generic implentation, while Gemm1 or Gemm2 select ck
implementation, the result of GemmSoftmaxGemmTunble affect by CK.
- Make tolerance more loosen.
- Add `GemmSoftmaxGemmPermuteGenericNestedTunable` to test Generic
implementation with tuning enabled.
### Description
<!-- Describe your changes. -->
Replace the constructor function `MLFloat16()` with the public member
function `FromBits()` in the file
`onnxruntime/core/providers/cann/cann_common.cc`
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
PR [#16506](https://github.com/microsoft/onnxruntime/pull/16506) changed
the public constructor function `MLFloat16(uint16_t x)` to private, and
added a public function `MLFloat16::FromBits(uint16_t x)` in the file
`include/onnxruntime/core/framework/float16.h`, which broke the CANN CI.
This PR aligns the CANN behavior with the modified class `MLFloat16`.
### Description
<!-- Describe your changes. -->
MAUI test app with tooling to add model and generated or provided input
test data.
The app will load the model and validate the output. It can also run a
specified number of iterations to provide basic performance information.
<img width="401" alt="image"
src="https://github.com/microsoft/onnxruntime/assets/979079/daf3af13-fb22-4cbb-9159-486b483a7485">
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Primarily to make it easier to test an arbitrary model on iOS. A MAUI
app allows testing on all platforms.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
#16506 Cause almost every translation units on linux complaint
```
[1175/1235] Building CXX object CMakeFiles/onnxruntime_test_all.dir/home/guangyunhan/onnxruntime/orttraining/orttraining/test/training_ops/cuda/softmax_test.cc.o
In file included from /home/guangyunhan/onnxruntime/include/onnxruntime/core/framework/float16.h:18,
from /home/guangyunhan/onnxruntime/include/onnxruntime/core/framework/data_types.h:17,
from /home/guangyunhan/onnxruntime/include/onnxruntime/core/framework/tensor.h:17,
from /home/guangyunhan/onnxruntime/onnxruntime/test/common/tensor_op_test_utils.h:16,
from /home/guangyunhan/onnxruntime/onnxruntime/test/providers/compare_provider_test_utils.h:7,
from /home/guangyunhan/onnxruntime/orttraining/orttraining/test/training_ops/cuda/softmax_test.cc:4:
/home/guangyunhan/onnxruntime/include/onnxruntime/core/session/onnxruntime_float16.h: In instantiation of ‘static constexpr uint16_t onnxruntime_float16::Float16Impl<Derived>::ToUint16Impl(float) [with Derived = onnxruntime::MLFloat16; uint16_t = short unsigned int]’:
/home/guangyunhan/onnxruntime/include/onnxruntime/core/framework/float16.h:42:66: required from here
/home/guangyunhan/onnxruntime/include/onnxruntime/core/session/onnxruntime_float16.h:241:7: note: ‘union onnxruntime_float16::detail::float32_bits’ has no user-provided default constructor
241 | union float32_bits {
| ^~~~~~~~~~~~
/home/guangyunhan/onnxruntime/include/onnxruntime/core/session/onnxruntime_float16.h:242:16: note: and the implicitly-defined constructor does not initialize ‘unsigned int onnxruntime_float16::detail::float32_bits::u’
242 | unsigned int u;
| ^
```
This PR shut the compiler up.
### Description
Some code was accidentally moved into the
`if(!params.is_cross_attention)' block, it must stay outside to work in
both cases.
### Motivation and Context
This causes invalid results. We detected this as a performance bug, as
it caused the EOS early exit to never happen, and the runs would always
take max_length to complete which was slow.
### Description
Mistake in beam scorer processing, atomicAdd result should be compared
with '1' vs '0' as it returns the original value, not the latest value.
This error just results in slow perf, nothing fails.
### Motivation and Context
Fixes#16642
DORT only select devices from inputs arguments' (type: torch.Tensor).
However, it errors out when a graph doesn't have any inputs (e.g., a
single aten::full graph). This PR address this problem by changing the
EP selection to
- First, inspect graph inputs. If there are some valid devices, use them
plus a default one (`OrtBackend.ep: str`).
- Otherwise, inspect graph outputs carried by `torch.fx.GraphModule` and
use all valid devices plus the default `OrtBackend.ep`.
- When both (1) and (2) fail, it uses the default EP specified by
`OrtBackend.ep`.
- Fix link errors by including the needed onnxruntime-extensions libraries in the static framework.
- Add Objective-C API to register custom ops from embedded onnxruntime-extensions.
Caveat: Not all onnxruntime-extensions build options are working yet. E.g., building with the onnxruntime-extensions OpenCV dependency does not work.
### Description
The SequenceMap function-op has a graph-attribute. ORT's
constant-folding optimization may identify constant-expressions inside
the subgraph and promote them to constants, stored as initializers in
the main graph. When it does this, the optimization updates the subgraph
to remove the corresponding nodes.
When we expand a SequenceMap node by inlining its function-expansion, we
need to use this updated subgraph. However, the existing code uses the
original graph-attribute (GraphProto), instead of regenerating it from
the modified subgraph. This results in producing a graph with duplicate
definitions for the constant-folded variable, resulting in an error
during graph-resolve.
This PR fixes this issue (just a single line fix), and adds a test-case
to cover this scenario.
---------
Signed-off-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
