### Share more constant initializers.
`ConstantSharing` transformer originally only handle single value
initializer (scalar or 1D).
This PR tried to share more cases to make common subexpression
elimination transformer to remove more duplicated nodes.
Originally, we used a single
vector<std::variant<float,half,int32,int64>> to store different scalar
values. In this PR, we create a unordered map with its key being
data_type + rank + element count, and its value is a vector of
`InitializerValue`.
For one specific initializer, if it fulfils the condition, then finally
will find the corresponding vector of `InitializerValue` by its
<data_type + rank + element count>, then search from the vector whether
the constant tensor already exist or not. After that, a value id is
returned, which will be combined together with <data_type + rank +
element count> to form the pattern key to decide which tensor to reuse
(legacy code).
### Motivation and Context
One example we see here is:
```mermaid
stateDiagram
[*] --> LayerNorm(b,s,64)
LayerNorm(b,s,64) --> Reshape1
Shape1_Const[b*s,64] --> Reshape1
LayerNorm(b,s,64) --> Reshape2
Shape2_Const[b*s,64] --> Reshape2
Reshape1 --> AttentionSubGraph
Reshape2 --> Add
AttentionSubGraph--> Add
Add --> [*]
```
Ideally CommonSubexpressionElimination can remove one of `Reshape1` and
`Reshape2`, while since `Shape1_Const` and `Shape2_Const` are different
NodeArg*, so it did not remove the duplication.
This is an example: removing the duplication will bring more
opportunities to apply graph transformations.
### Description
Add hipBLASLt to GEMM Tunable op, which supports GEMM and
StridedBatchedGEMM.
To enable hipBLASLt implementation, add an extra flag to the building
command: `--cmake_extra_defines onnxruntime_USE_HIPBLASLT=ON`.
SkipLayerNorm fusion fuses LayerNorm and one or more Add kernels now.
While LayerNormalization kernel allows different input and output type
by definition, SkipLayerNormalization must have the same input and
output type.
This graph is valid as the output of Add node is float16 and two inputs
from initializers are float.
But, when Add and LayerNormalization are fused, it fails because two
inputs of Add node are float16 type and SkipLayerNormalization must have
the same input types. To avoid this failure, this PR adds Cast node
before inputs of SkipLayerNormalization when input and output type are
different and output type is float. The above graph is fused as follows,
For performance, it'd better for SkipLayerNormalization to support
different input and output type, but this PR is to unblock Turing NLR v5
base mode in Babel. When we have more cases, we can support it.
Add support to use sequence as input ids in decoder inputs to Beam
Search CUDA Op
### Description
Currently Beam search Op is only supported for CPU EP, added support for
CUDA EP.
### Motivation and Context
- For Turing models inference was throwing segmentation fault due to
copy failing in cuda memory, also beam search support was not present in
cuda.
### Description
1. Disable XNNPack EP's tests in Windows CI pipeline
The EP code has a known problem(memory alignment), but the problem does
not impact the usages that we ship the code to. Now we only use XNNPack
EP in mobile apps and web usages. We have already pipelines to cover
these usages. We need to prioritize fixing the bugs found in these
pipelines, and there no resource to put on this Windows one. We can
re-enable the tests once we reached an agreement on how to fix the
memory alignment bug.
2. Delete anybuild.yml which was for an already deleted pipeline.
3. Move Windows CPU pipelines to AMD CPU machine pools which are
cheaper.
4. Disable some qdq/int8 model tests that will fail if the CPU doesn't
have Intel AVX512 8-bit instructions.
### Description
Temporarily disable BatchNormalizationGrad test due to random failure.
Example:
```
2023-04-12T06:33:24.1593811Z 1: [ RUN ] GradientCheckerTest.BatchNormalizationGrad
2023-04-12T06:33:27.5603881Z 1: D:\a\_work\1\s\orttraining\orttraining\test\gradient\gradient_ops_test.cc(1468): error: Value of: IsErrorWithinTolerance(max_error, error_tolerance)
2023-04-12T06:33:27.5604509Z 1: Actual: false
2023-04-12T06:33:27.5604719Z 1: Expected: true
2023-04-12T06:33:27.5604997Z 1: max_error: 1.776702880859375; tolerance: 0.019999999552965164; ORT test random seed: 2552121240;
2023-04-12T06:33:27.5605266Z 1: Google Test trace:
2023-04-12T06:33:27.5605531Z 1: D:\a\_work\1\s\onnxruntime\test\common\tensor_op_test_utils.cc(14): ORT test random seed: 8910
2023-04-12T06:33:27.5605843Z 1: D:\a\_work\1\s\onnxruntime\test\common\tensor_op_test_utils.cc(14): ORT test random seed: 5678
2023-04-12T06:33:27.5606478Z 1: D:\a\_work\1\s\onnxruntime\test\common\tensor_op_test_utils.cc(14): ORT test random seed: 1234
2023-04-12T06:33:27.8285560Z 1: D:\a\_work\1\s\orttraining\orttraining\test\gradient\gradient_ops_test.cc(1493): error: Value of: IsErrorWithinTolerance(max_error, error_tolerance)
2023-04-12T06:33:27.8286181Z 1: Actual: false
2023-04-12T06:33:27.8286404Z 1: Expected: true
2023-04-12T06:33:27.8286669Z 1: max_error: 1.776702880859375; tolerance: 0.019999999552965164; ORT test random seed: 2552121240;
2023-04-12T06:33:27.8286942Z 1: Google Test trace:
2023-04-12T06:33:27.8287208Z 1: D:\a\_work\1\s\onnxruntime\test\common\tensor_op_test_utils.cc(14): ORT test random seed: 8910
2023-04-12T06:33:27.8287532Z 1: D:\a\_work\1\s\onnxruntime\test\common\tensor_op_test_utils.cc(14): ORT test random seed: 5678
2023-04-12T06:33:27.8287849Z 1: D:\a\_work\1\s\onnxruntime\test\common\tensor_op_test_utils.cc(14): ORT test random seed: 1234
2023-04-12T06:33:51.6368960Z 1: [ FAILED ] GradientCheckerTest.BatchNormalizationGrad (27475 ms)
```
### Description
The following three lines are needed before including some cutlass
header files, because cutlass uses "and"/"or" keywords. Generally it
should not be a problem without this header, but nvcc is not strictly
compliant to C++ standard.
```c++
#ifdef __cplusplus
#include <ciso646>
#endif
```
We didn't hit this problem because the above code exists in absl. We
always include absl headers first. However, ABSL recently deleted them!
https://github.com/abseil/abseil-cpp/pull/1246
The cutlass dependency was introduced in #14343 , after we had abseil.
### Optimize SCE loss compute
Compute optimization based on label data sparsity:
- Insert ShrunkenGather before SCELoss node, to filter out invalid
labels for compute.
- Support ShrunkenGather upstream.
- Added test for the above.
- Added flag to enable label sparsity optimization with env var, by
default disabled now. Will enable after comprehensive benchmarking
later.
- Extract common logic into test_optimizer_utils.h/cc from
core/optimizer/compute_optimzier_test.cc, then the common functions can
be shared by both core/optimizer/compute_optimzier_test.cc and
orttraining/core/optimizer/compute_optimzier_test.cc
- Extract common logic into shared_utils.h/cc: `GetONNXOpSetVersion` and
`Create1DInitializerFromVector`
### 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
The code handling variadic parameters when creating a schema for a
function has a minor bug.
The checking logic was nested inside a conditional, instead of being
outside.
Fix the logic, and add a test-case. This bugs manifests itself when the
first parameter in the
variadic list is not an input/output of the enclosing function.
### Motivation and Context
Fixes https://github.com/microsoft/onnxruntime/issues/15404
---------
Signed-off-by: Ganesan Ramalingam <grama@microsoft.com>
### Description
<!-- Describe your changes. -->
* Integrate TRT 8.6EA on relevant Linux/Windows/pkg pipelines
* Update onnx-tensorrt to 8.6
* Add new dockerfiles for TRT 8.6 and clean old ones
* Update
[CGManifest](https://github.com/microsoft/onnxruntime/tree/main/cgmanifests)
files and ort build deps version
* yml/script update
* Enable built-in TRT parser option on TRT related pipelines by default
* Exclude test TopKOperator.Top3ExplicitAxisInfinity out of TRT EP tests
(8.6-EA has issue with topk operator)
This change moves the DML CI pipeline to the A10 machines and fixes or
disables tests that were failing from this change.
- Max error rate threshold was increased for Image Tests
- Some failing batch tests were disabled
---------
Co-authored-by: Changming Sun <chasun@microsoft.com>
### Description
Recently Visual Studio and python started to provide native Windows
ARM64 packages. This PR is to provide better support for building on
Windows ARM64. You can do it as what you did for x64. Like:
```
python tools\ci_build\build.py --config Debug --update --skip_submodule_sync --build_dir b --cmake_generator "Visual Studio 17 2022"
```
You do not need to append the "--arm64" build arg, and do not need to
cross-compile protoc for a different arch as you are not cross-compiling.
**caveat:** it does not work with the latest cmake release(3.26.x). It
only works fine with cmake 3.25.x and below. Filed a bug to them:
https://gitlab.kitware.com/cmake/cmake/-/issues/24797
### Motivation and Context
Provide better support for building on Windows ARM64.
### Description
<!-- Describe your changes. -->
As title
### 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. -->
https://github.com/microsoft/onnxruntime/issues/15110
---------
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
### Description
add script to validate generated NPM packages and publish it to
artifacts, so that release pipeline can use it.
once this PR is merged, I will update the NPM package release pipeline.
### Description
Implement Optional Type metadata support in the library.
Implement optional support in C# API along with metadata.
Implement Sequence, Map, Optional test data support
and test execution.
Prune tests and provide more details for failing tests in C# code.
Note, this PR does not enable running onnx test models in C++.
### Motivation and Context
Opset18 optional type support.
Add support for kMSInternalNHWCDomain and kPytorchAtenDomain op domains to op reduction script.
Make it an error if the op reduction script encounters unknown op domains.
### Description
<!-- Describe your changes. -->
1. enabled self-attention fusion in mt-5 decoder graph
2. fix a parity issue
https://github.com/microsoft/onnxruntime/issues/15042
### 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. -->
---------
Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
CP: [For HLSL shader ops in the DirectML EP (STFT,DFT) FP16 ops should
fallback to CPU when there is no hardware support #15414
](https://github.com/microsoft/onnxruntime/pull/15414)
For HLSL shader ops in the DirectML EP (STFT,DFT) FP16 ops should
fallback to CPU when there is no hardware support.
## Description
Implements support for LeakyReLU in ActivationOpBuilder for CoreML's EP.
### Motivation and Context
This speeds up inference on macOS significantly for models using
LeakyReLU.
### Description
optimize default session options parsing.
- do minimal property assignment to the passed in `options` object.
- modify default value of `enableCpuMemArena` and `enableMemPattern` to
`false`. We don't get benefits from enabling these 2 flags in web
assembly
### Description
1. Move it to a separated pool that use the same image as [the public
hosted
pool](https://learn.microsoft.com/en-us/azure/devops/pipelines/agents/hosted?view=azure-devops&tabs=yaml).
Also, create a beta pool which contains the next version image of the
hosted pool, and add jobs in our post merge pipeline to test if the next
version image will break our CI. So, usually we will have at least one
week to prepare.
2. Change the cmake generator in use in our pipelines from "Ninja" to
"MingW Makefile", because the latest version of cmake doesn't work with
the latest version of Ninja. People who prefer Ninja could still use
ninja in their local build by passing "--cmake_generator ninja" to
[build.py](https://github.com/microsoft/onnxruntime/blob/main/tools/ci_build/build.py).
3. Delete eager mode CI pipeline.
### Motivation and Context
I need to update the software we have in our CI build machines, and I
need to resolve this incompatibility issue. In more detail, the build
error I hit was:
em++: error:
CMakeFilesonnxruntime_mlas_test.dirC_a_work1sonnxruntimetestmlasunittesttest_activation.cpp.o:
No such file or directory
("CMakeFilesonnxruntime_mlas_test.dirC_a_work1sonnxruntimetestmlasunittesttest_activation.cpp.o"
was expected to be an input file, based on the commandline arguments
provided)
After this PR we will deprecate python 3.7 support. The eager mode CI
pipeline is the last one that still use python 3.7. Then we can rework
the PR #10953 made by [fs-eire](https://github.com/fs-eire) last year.
Fixed
[AB#14435](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/14435)
### Description
Adds VIT model type to the benchmark
Also adds Swin (v1) model type
### Motivation and Context
Image models are important and we should verify these work as expected
at the performance we expect.
**Description**: Register an implementation for BatchNormInternal and
add a CPU kernel for BatchNormGradient. This is the third in a series of
PRs to implement BN training on CPU (first was #6946, second was #7539).
**Motivation and Context**
Support training networks with BatchNorm (e.g. convnets). Also note that
there exists a CUDA kernel for BN (forward training & backwards) but
it's currently disabled due to flaky failures; someone more familiar
with those parts can register the implementation for BNInternal on CUDA
(gradient kernel doesn't have to change).
---------
Co-authored-by: Simon Zirui Guo <simonguozirui@berkeley.edu>
Co-authored-by: mindest <linminuser@gmail.com>
Co-authored-by: mindest <30493312+mindest@users.noreply.github.com>
### Description
<!-- Describe your changes. -->
Update the support deepspeed to 0.8.3 as it's the latest version
### 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 will fix the error of `Skip modifying optimizer because of
unsupported DeepSpeed version`
Co-authored-by: ruiren <ruiren@microsoft.com>
Add workflow to update Objective-C API docs. Remove the Objective-C API doc generation step from the packaging pipeline.
There are similar workflows for automatically updating other language API docs. This change enables this for Objective-C too.
