Add ga to python apidocs - #42
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### Description It missed a space there. ### Motivation and Context Right now the pipeline is failing because GSL was just converted from a submodule to a cmake external project.
### Description Delete CUB ### Motivation and Context Because it is already in CUDA SDK.
The oneDNN 2.7.1 release includes multiple functional and performance improvements. Signed-off-by: George Nash <george.nash@intel.com> ### Description Update the oneDNN library from 2.7.0 to 2.7.1. This contains multiple functional and performance improvements. ### Motivation and Context This is a minor point release from the oneDNN library that gives performance and functional fixes that were found in the oneDNN 2.7 library shortly after release. Signed-off-by: George Nash <george.nash@intel.com>
**Description**: Subgraph-level recompute This PR adds an optional capability trading additional re-computation for better memory efficiency. Specifically, a pre-defined operator list used to iterate the Graph to find some subgraphs for recompute, to reduce some stashed activations whose lifetime across forward and backward pass. When training with ORTModule, by default, the graph transformer will scan the execution graph to find all eligible subgraph to recompute, along with sizes that can save. An example looks like below. If we want to enable some of them to recompute, we can define env variable this way: `export ORTMODULE_ENABLE_MEMORY_ALLEVIATION="Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+:1:-1,BiasGelu+:1:-1,BitmaskDropout+Cast+:1:-1,FusedMatMul+:1:-1,Cast+:1:-1,Mul+Add+:1:-1,Mul+Sub+:1:-1"` ``` [1,0]<stderr>:2,022-10-12 14:47:39.302,954,530 [W:onnxruntime:, memory_alleviation.cc:595 PrintSummary] [1,0]<stderr>:MemoryAlleviation Summary: [1,0]<stderr>: User config: [1,0]<stderr>: Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+:1,BiasGelu+:1,BitmaskDropout+Cast+:1,FusedMatMul+:1,Cast+:1,Mul+Add+:1,Mul+Sub+:1 [1,0]<stderr>: ================================= [1,0]<stderr>: Subgraph: BitmaskDropout+ [1,0]<stderr>: AlleviationType: Disabled [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 1,024 x Frequency:1 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: BiasGelu+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x input_ids_dim1 x 4,096 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Reshape[1,0]<stderr>:+ [1,0]<stderr>: AlleviationType: Disabled [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:labels_dim0 x Frequency:1 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Unsqueeze+Unsqueeze+Cast+Sub+Mul+Mul+FusedMatMul+Cast+Add+BiasSoftmaxDropout+Cast+ [1,0]<stderr>: AlleviationType: Disabled [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x input_ids_dim1 x Frequency:23 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x input_ids_dim1 x Frequency:1 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Mul+Add+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 1 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: FusedMatMul+Cast+Add+Reshape+Cast+ [1,0]<stderr>: AlleviationType: Disabled [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 2 x 4 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Mul+Sub+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 1 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Cast+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:1,024 x 1,024 x Frequency:97 [1,0]<stderr>: PatternShape:3 x 1,024 x Frequency:1 [1,0]<stderr>: PatternShape:8 x 64 x Frequency:24 [1,0]<stderr>: PatternShape:1,024 x 4,096 x Frequency:24 [1,0]<stderr>: PatternShape:4,096 x Frequency:24 [1,0]<stderr>: PatternShape:4,096 x 1,024 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: FusedMatMul+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x input_ids_dim1 x 4,096 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: ================================= ``` "Type config:" whether recompute is enabled by users. 0 - disable, 1- enable. "Subgraph" means what kind of subgraph will be recomputed, in this case, it is a single node "Gelu", and it will be "Recompute". "Shape && Frequency" means, for this recompute, one tensor of size (batch size, 500) will be saved because it will be recomputed. **Baseline** On a 1P model (DEBERTA V2), sequence length 256, training with 16 A100 GPUs. With latest main branch, we can run batch size 16, and the maximum batch size < 32. So 16 is usually chosen by data scientists. 65% of 40GB memory is used during training. The SamplesPerSec=479.2543353561354.  **With this PR** Gelu is recomputed for saving memory peak, batch size 32 can be run. The 97% of 40GB A100 is used, the SamplesPerSec=562.041593991271 (**1.17X** of baseline).  **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 <!-- Describe your changes. --> ### Motivation and Context these workflows are prone to timeout.
1. Update CK to its latest develop branch 2. `-mllvm -amdgpu-early-inline-all=true` is critical to CK's performance, ensure it is properly configured. - The flags are propagated from target `hip-lang::device`'s `INTERFACE_COMPILE_OPTIONS`, we must not manually add the flags. - Instead, we must ensure this target is properly configured by checking _CMAKE_HIP_DEVICE_RUNTIME_TARGET is set. TL,DR `hip-lang::device` sometime will be not be properly configured if our `CMAKE_PREFIX_PATH` is not configured carefully. In the CI docker, the configuration is in good state, but on dev machine it is not, which then silently result poor performance for kernels. We fixed it in this PR and add a guard to avoid unsuccessful future editing and to prevent convoluted debugging process. `_CMAKE_HIP_DEVICE_RUNTIME_TARGET ` is shared in `/opt/rocm/lib/cmake/hip-lang/hip-lang-config.cmake` and it is internal to [CMake](https://gitlab.kitware.com/cmake/cmake/-/merge_requests/6121/diffs), the variable name will not be changed in the foreseeable future.
### 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. -->
Customer reported this issue: they see many warnings when doing hte evaluation using ORTModule.  After investigation, we found the `training_mode` is exported to a wrong value in evaluation mode, it's value should be 0, but we found it is 1. Fix: fix pythonop training mode if training_mode's type is torch._C._onnx.TrainingMode, then not matter it is EVAL or TRAINING, "if training_mode" will always be true
### Description The failed tests are: ``` [ FAILED ] ModelTests/ModelTest.Run/cpu__models_zoo_opset7_ResNet101_DUC_HDC_ResNet101DUC7, where GetParam() = L"cpu_..\\models\\zoo\\opset7\\ResNet101_DUC_HDC\\ResNet101-DUC-7.onnx" [ FAILED ] ModelTests/ModelTest.Run/cpu__models_zoo_opset12_ResNet101_DUC_HDC12_ResNet101DUC12, where GetParam() = L"cpu_..\\models\\zoo\\opset12\\ResNet101_DUC_HDC-12\\ResNet101-DUC-12.onnx" [ FAILED ] ModelTests/ModelTest.Run/cpu__models_zoo_opset11_FCN_ResNet101_model, where GetParam() = L"cpu_..\\models\\zoo\\opset11\\FCN ResNet-101\\model.onnx" [ FAILED ] ModelTests/ModelTest.Run/cpu__models_zoo_opset10_SSD_model, where GetParam() = L"cpu_..\\models\\zoo\\opset10\\SSD\\model.onnx" ``` They are instable. Sometimes they fail with error "Message: bad allocation". Sample job: https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=797861&view=logs&j=cceb3ef3-4a22-5fef-c5e9-ef6abe6579ed&t=fa89271b-d780-55e6-8822-71317e62ce21
### Add guidelines for ORTModule As title. Feel free to let me know if I missed something. ### 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 adds bfloat16 support to the oneDNN ep.
When using the oneDNN ep this enables bfloat16 support for the following
ops:
Exp, Sigmoid, Tanh, Relu, MatMul, Gelu, BiasGelu, Add, Sub,
Mul, Div, Div, Sqrt, Pow, ReduceMean, Abs, Cast, Equal, Exp,
FastGelu, FusedMatMul, Gemm, Greter, GreaterOrEqual, LeakyRelu,
Less, LessOrEqual, LRN, ReduceOps, Reshape, Squeeze, Transpose,
and Unsqueeze.
LayerNorm with some internal casting.
BatchNorm only enabled BFloat16 for input and output, scale and bias
still need fp32 input.
Added bfloat16 unit tests for all of the operators in question. When
possible we reused the already existing unit tests that were added by
CUDA and ROCM eps.
In many of the unit tests an unusual pattern will be seen
#if defined(USE_DNNL)
TEST(Test, bfloat16_test) {
#if defined(USE_DNNL)
// oneDNN ep specific code
#endif
//test code
}
#endif
Although it looks unusual this was purposely done if another ep
implements bfloat16 support for that operator they will be able to
enable the unit test by adding there execution provider to the first
line without needing to edit inside the test.
Example: `#if defined(USE_CUDA) || defined(USE_DNNL)` see the
MatMul_float16 test in matmul_test.cc for and example of how this is
useful.
Additionally two new ISA checks (AVX512_BF16 and AMX-BF16) were added to
the cpuid_info code in. This was important to detecting is bfloat16
operations are supported by the CPU.
### Motivation and Context
This expands the capabilities of the oneDNN execution provider to
support models containing bfloat16 operations.
Signed-off-by: George Nash <george.nash@intel.com>
Signed-off-by: Ruihan-Yin <ruihan.yin@intel.com>
### Description * Add getter/setter to access and update C# OrtEnv log level * Add C API about updating ort env with custom log level to support the setter above (Following [pybind implementation](https://github.com/microsoft/onnxruntime/blob/952c99304a764328070ac6c07f510deec06dedf4/onnxruntime/python/onnxruntime_pybind_state.cc#L923-L924)) * Add test case to verify getter & setter ### Motivation and Context * For C++/Python, the log level can be adjusted via OrtEnv, and this feature is missing in C# binding
### Description Upgrade cmake version to 3.24 because I need to use a new feature that is only provided in that version and later. Starting from cmake 3.24, the [FetchContent](https://cmake.org/cmake/help/latest/module/FetchContent.html#module:FetchContent) module and the [find_package()](https://cmake.org/cmake/help/latest/command/find_package.html#command:find_package) command now support integration capabilities, which means calls to "FetchContent" can be implicitly redirected to "find_package", and vice versa. Users can use a cmake variable to control the behavior. So, we don't need to provide such a build option. We can delete our "onnxruntime_PREFER_SYSTEM_LIB" build option and let cmake handle it. And it would be easier for who wants to use vcpkg. ### Motivation and Context Provide a unified package management method, and get aligned with the community. This change is split from microsoft#13523 for easier review.
### Description The yum repo is called: ["Extra Packages for Enterprise Linux (EPEL)"](https://docs.fedoraproject.org/en-US/epel/#what_is_extra_packages_for_enterprise_linux_or_epel) . It is provided by Fedora community for RHEL/CentOS/... Linux distros. However, we do not really need it. ### Motivation and Context To minimize the number of dependencies. And the command "yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm" often fails because the website is often not responding,
1. Remove the cmake option onnxruntime_DEV_MODE and replace it with "--compile-no-warning-as-error" 2. Suppress some GSL warnings because now we treat nvcc diag warnings as errors
### Description In the TVM EP, this adds more entries to the conversion from `ONNXTensorElementDataType` to `DLDataType`. Additionally, it removes an unused function and updates the TVM revision to allow running models from recent revisions of TVM. ### Motivation and Context In the TVM EP, the mapping from `ONNXTensorElementDataType` to `DLDataType` was incomplete and neglected several integer types (in particular `ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8` and `ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8`) which prevented some models from running. Co-authored-by: Peter Salas <psalas@octoml.ai>
microsoft#13393) The old runtime optimization format is not readily convertible to the new one without extra information for translating kernel def hashes. Ignore such saved runtime optimizations and output a warning for now.
Pytorch was added to inference pipelines in PR microsoft#8027. But, actually these pipelines do not use PyTorch. PyTorch is huge, here we need to install it for 4 different Python versions. If we remove PyTorch, we will significantly reduce the image size. And, now downloading a pytorch package often takes more than 1 hour. If we do it 4 times, it may take 4 hours. Valgrind was added by me long time back, and it was not used too. Now we run Linux tests outside of docker containers. So, when we have the need, we could install it through apt-get on Ubuntu instead of doing it in the CentOS container.
### Description Properly cleans up all temporary resources created while running benchmarks. Details: - Dump all temporary artifacts (TRT engines, TRT profiles, inference profiles, fp16 models) into a temp directory in `/tmp/`. Each model/EP combination has its own temp directory that is deleted after validation and benchmarking. - Allow running both validation and benchmarking in one invocation of the benchmark.py script. This is necessary to allow the benchmarking step to reuse artifacts (e.g., TRT engines) created during validation. Before this PR, we ran validation on all model/EP combinations before running benchmarks on all combinations again. This required us to keep all temporary artifacts for all model/EP combinations throughout the entire run (expensive). - Create individual functions for validation and benchmarking (split-up large function that did it all) ### Motivation and Context The EP Perf pipeline failed to run because the script generated too much output and the VM ran out of disk space.
…icrosoft#13587) ### Description Fix microsoft#13509 ### Motivation and Context The exception was caused by the incorrect fetches, which was from the binding with last test cases. https://github.com/microsoft/onnxruntime/blob/efcbdac58e542273c7cd42f5e5bcd01c0218f0e3/onnxruntime/core/session/onnxruntime_c_api.cc#L809-L815
### Description Add a DML registration for Shape to avoid copying back to the CPU just to get the shape of a GPU tensor. ### Motivation and Context When using free dimensions, many Transformers models extensively use the `Shape` operator. This causes hundreds of GPU->CPU copy that should be completely avoidable. Note that this change also uses the same heuristics as other providers (e.g. CUDA) to force some tensors on the CPU in certain situations. Co-authored-by: Patrice Vignola <pavignol@microsoft.com>
### Description Redo the round using gsl:narrow and SafeInt ### 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. -->
On AMD Instinct MI200 GPUs, the FP16 and BF16 V_DOT2 and MFMA matrix instructions flush input and output denormal values to zero. When training using FP16 precision, some models may fail to converge with FP16 denorms flushed to zero. The affected instructions are only used by rocBLAS (GEMM) and MIOpen (convolution) kernels; all other onnxruntime operations will not encounter this behavior. All other supported AMD GPUs will not encounter this behavior. rocBLAS and MIOpen provide alternate implementations for affected FP16 operations. Alternate implementations for BF16 operations are not provided; BF16 numbers have a larger dynamic range than FP16 numbers and are less likely to encounter denormal values. For the FP16 alternate implementations, FP16 input values are cast to an intermediate BF16 value and then cast back to FP16 output after the accumulate FP32 operations. In this way, the input and output types are unchanged. Denormal values more frequently occur in the backward pass of training during gradient calculation. Therefore, it is necessary to track when the backward pass of training is executing. For the ROCm EP only, the `__backwardpass` attribute is added to all Nodes after the YieldOp is detected. This takes place in a level1 graph optimization pass. The attribute is forwarded to any newly created FusedMatMul Nodes. In addition, the scope-based helper class `BackwardPassGuard` is provided to toggle state for rocblas. This behavior of using the alternate implementations during the backward pass is made automatic with this PR. This default behavior can be overridden using environment variables, ROCBLAS_INTERNAL_FP16_ALT_IMPL and MIOPEN_DEBUG_CONVOLUTION_ATTRIB_FP16_ALT_IMPL. The behavior of these environment variables is as follows: | | forward | backward | |--------------|-----------|-----------| | Env unset | original | alternate | | Env set to 1 | alternate | alternate | | Env set to 0 | original | original | See also: https://pytorch.org/docs/stable/notes/numerical_accuracy.html#reduced-precision-fp16-and-bf16-gemms-and-convolutions-on-amd-instinct-mi200-devices
### Description <!-- Describe your changes. --> Introduce Gemm weights pre-pack. ### Motivation and Context A 1-P customer requested a performance improvement for DeepGru which consumes a bulk of CPU in their model. This provides measurable performance improvements. Customer model numbers. gru: mean = 356 us; 1ms = 99.8 prctile; 99th prctile = 665 ms (yuslepukhin/deep_gru_opt) main: mean = 375 us; 1ms = 99.8 prctile; 99th prctile = 695 ms (where yuslepukhin/deep_gru_opt branched off main) 1.13.1: mean = 391 us; 1ms = 99.6 prctile; 99th prctile = 744 ms
### 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. -->
…icrosoft#13734) ### Description Add mixed datatype support for DML's LayerNorm contrib op. ### Motivation and Context The fusion logic removes casts around LayerNorm in the graph because the contrib version of the op supports mixed datatypes. Scale, Bias and Output's datatypes must match, but input's datatype can be different.
…lptr) (microsoft#13818) ### Description Add support for LayerNorm scale == nullptr != bias == nullptr
### Description This PR implements the **Concat Operator** for the **OneDNN Execution Provider**. ### Motivation and Context - As part of evaluating ORT performance on ARM based targets such as Graviton3, we discovered that the OneDNN EP had some gaps on operator coverage. - The Concat Operator is fairly common and used in models such as Yolov5, MobileNet, DistillBert and GPT2 - For Yolov5 specifically, this improves average inference time over 100 runs on Graviton3 from 180.2ms to 115.5ms when using OneDNN + ARM Compute Library. Co-authored-by: Gaz Iqbal <giqbal@octoml.ai>
### Description Add the NonZero op for DML ### Motivation and Context NonZero is used in a few transformer models, so having a DML implementation will stop large tensors from being transferred to the CPU and back to the GPU
### Description GetInputMemoryType was introduced in ver 13 in [this PR](microsoft#10879). The ver check introduced in this PR allows custom ops compiled using older versions to work with newer versions (> 12) of the ORT binary. ### Motivation and Context Fixes binary compatibility.
### Description fix a wrong assert condition in benchmark_helper.py (introduced in microsoft#13455)
### Description <!-- Describe your changes. --> Testcases of LayerNorm with fp16/bf16 are failed in Andriod and IOS since the two platforms don't support the combinations of datatypes as well. https://dev.azure.com/onnxruntime/onnxruntime/_build?definitionId=134&_a=summary https://dev.azure.com/onnxruntime/onnxruntime/_build?definitionId=53&_a=summary ### 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 enables building nuget packages locally for on device training using --build_nuget arg. This PR also enables the C# bindings by default in the managed package. If a user triggers any training apis when the native binary is not built for training, an exception with message "Training is disabled in the current build. Please build ONNXRuntime from source with the build flags enable_training and enable_training_on_device. " is thrown. Build command for creating nuget packes for on device training: build.bat --enable_training --enable_training_on_device --build_nuget 2 Nuget packages are built 1. Microsoft.ML.OnnxRuntime.Managed 2. Microsoft.ML.OnnxRuntime.Training OR Microsoft.ML.OnnxRuntime.Training.Gpu ### 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 <!-- 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. -->
### Description Use CCache and ADO CacheTask to Accelerate MacOS build. ref: https://learn.microsoft.com/en-us/azure/devops/pipelines/release/caching?view=azure-devops ### Motivation and Context The MacOS CI duration could be reduced from more than **70minutes** to **10 minutes** https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=824912&view=results
Upgrade pybind11 to include the fix for microsoft#9735
…oft#13852) Fix various flaky build error due to onnxruntime_session missing dependencies on hipify generated files.
### Description Add SkipLayerNormalization for the DML EP
### Description ### Motivation and Context Test data in the image has been fixed.
microsoft#13874) This reverts commit 87d5703. ### Motivation and Context There was a bug in Linux CUDA installation. The OS image is updated. The TestCUDAProviderOptions could be reenabled.
### Description Decouple strided tensor support from ENABLE_TRAINING ### Motivation and Context This is step 1 for creating a dedicated build for on device training. Intention is 1. We can set ENABLE_STRIDED_TENSORS in cmake when either ENABLE_TRAINING or ENABLE_TRAINING_ON_DEVICE is selected, this way we dont have to use if defined(ENABLE_TRAINING) || defined(ENABLE_TRAINING_ON_DEVICE ) everywhere in the code. 2. This also paves the way to easily enable strided tensor support for inference in future (if required).
### Description Exposing get and set Learning rate for optimizer ### Motivation and Context you can now set learning rate for optimizer.
Bumps [minimatch](https://github.com/isaacs/minimatch) from 3.0.4 to 3.0.5. <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/isaacs/minimatch/commit/707e1b231d5ddf5b00040bd04968a1a092992d1a"><code>707e1b2</code></a> 3.0.5</li> <li><a href="https://github.com/isaacs/minimatch/commit/a8763f4388e51956be62dc6025cec1126beeb5e6"><code>a8763f4</code></a> Improve redos protection, add many tests</li> <li><a href="https://github.com/isaacs/minimatch/commit/bafa295617d984c5863b13a33b2bb601c67e4a8b"><code>bafa295</code></a> Use master branch for travis badge</li> <li><a href="https://github.com/isaacs/minimatch/commit/013d64dc242213bab1cf090d4a9e6bdf02f61160"><code>013d64d</code></a> update travis</li> <li>See full diff in <a href="https://github.com/isaacs/minimatch/compare/v3.0.4...v3.0.5">compare view</a></li> </ul> </details> <br /> [](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) - `@dependabot use these labels` will set the current labels as the default for future PRs for this repo and language - `@dependabot use these reviewers` will set the current reviewers as the default for future PRs for this repo and language - `@dependabot use these assignees` will set the current assignees as the default for future PRs for this repo and language - `@dependabot use this milestone` will set the current milestone as the default for future PRs for this repo and language You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/microsoft/onnxruntime/network/alerts). </details> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
### Description fix session option setting of memory pattern.
### Description - Use same data type as input for mask_index tensor which is used as DML GEMM API's C parameter. - Remove gsl header include as it is already gets included transitively. ### Motivation and Context - Why is this change required? What problem does it solve? Bug found in internal conformance testing. - If it fixes an open issue, please link to the issue here. N/A
Always build with XNNPACK EP in iOS CI build. Combine builds for CPU, CoreML, and XNNPACK EPs due to limited build agent resources.
### Description <!-- Describe your changes. --> 1. Remove ROCm5.3 pipeline because it has rocblas bug, we don't need it. 2. We removed the dependency on centos docker image provided by AMD(https://hub.docker.com/r/rocm/dev-centos-7) and build ROCm centos base image by ourselves. The reference dockerfile(https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/dev/Dockerfile-centos-7) is very redundant for our need. We simplified the ROCm manylinux dockerfile. 3. Different versions of rocm use the same dockerfile `Dockerfile.manylinux2014_rocm`. ### 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: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
…cSelection (microsoft#13758) ### Description <!-- Describe your changes. --> 1. Update FastGelu conditions for supported parameters, avoid redundant configurations participating in tuning。 2. Add kernel explorer test for FastGeluStaticSelection ### 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: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### Description disable closure compiler in debug build. after this change, emscripten will only run closure compiler in release build.
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