This var has been initialized to 0 in tint, so no need extra loop to do
it again:
```
float tint_symbol_52[1][4] = (float[1][4])0;
{
for(int tint_symbol_53 = 0; (tint_symbol_53 < 1); tint_symbol_53 = (tint_symbol_53 + 1)) {
{
for(int tint_symbol_54 = 0; (tint_symbol_54 < 4); tint_symbol_54 = (tint_symbol_54 + 1)) {
tint_symbol_52[min(uint(tint_symbol_53), 0u)][min(uint(tint_symbol_54), 3u)] = 0.0f;
}
}
}
}
```
### 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
the exception was caused by
3dd6fcc089
Why I add skip_macos_test
because there's new an exception in
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1425579&view=logs&j=c90c5af3-67d5-5936-5a62-71c93ebfca65&t=01038f35-8e78-5801-1aa1-d9647bb65858
```
2024-07-05T14:41:09.3864740Z mkdir -p /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Products/Debug/macos_package_testUITests.xctest/Contents/Frameworks
2024-07-05T14:41:09.3933430Z mkdir: /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Products/Debug/macos_package_testUITests.xctest: Operation not permitted
2024-07-05T14:41:09.3996760Z /var/folders/0f/b0mzpg5d31z074x3z5lzkdxc0000gn/T/tmp97ycvwq5/apple_package_test/Pods/Target Support Files/Pods-macos_package_testUITests/Pods-macos_package_testUITests-frameworks.sh: line 7: realpath: command not found
2024-07-05T14:41:09.4003170Z :18: error: Unexpected failure
2024-07-05T14:41:11.1323470Z error: Sandbox: mkdir(72212) deny(1) file-write-create /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Products/Debug/macos_package_testUITests.xctest (in target 'macos_package_testUITests' from project 'apple_package_test')
2024-07-05T14:41:11.1325620Z
2024-07-05T14:41:11.8731110Z
2024-07-05T14:41:11.8733040Z Test session results, code coverage, and logs:
2024-07-05T14:41:11.8734820Z /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Logs/Test/Test-macos_package_test-2024.07.05_14-40-38-+0000.xcresult
2024-07-05T14:41:11.8735530Z
2024-07-05T14:41:11.8906210Z Testing failed:
2024-07-05T14:41:11.8911060Z Sandbox: mkdir(72212) deny(1) file-write-create /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Products/Debug/macos_package_testUITests.xctest
2024-07-05T14:41:11.8912570Z Unexpected failure
2024-07-05T14:41:11.8913690Z Testing cancelled because the build failed.
2024-07-05T14:41:11.8914380Z
2024-07-05T14:41:11.8914970Z ** TEST FAILED **
2024-07-05T14:41:11.8915480Z
2024-07-05T14:41:11.8915780Z
2024-07-05T14:41:11.8916750Z The following build commands failed:
2024-07-05T14:41:11.8919280Z PhaseScriptExecution [CP]\ Embed\ Pods\ Frameworks /Users/runner/Library/Developer/Xcode/DerivedData/apple_package_test-akksnidsbpojopfdqrclgsoqqerv/Build/Intermediates.noindex/apple_package_test.build/Debug/macos_package_testUITests.build/Script-059136A7770CA5376C30F2FD.sh (in target 'macos_package_testUITests' from project 'apple_package_test')
2024-07-05T14:41:11.8922180Z (1 failure)
```
And I find macos test is skipped in
9ef28f092f/tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml (L119-L127)
as well.
Maybe it is an known issue.
### Description
Repeat of #21084 with removal of policy CMP0144 to suppress warnings
which uses CMake 3.27.0.
### Motivation and Context
Already approved PR:
https://github.com/microsoft/onnxruntime/pull/21084
Removed the added policy from CMake 3.27.0.
### Description
The implementation inside EP requires registering some custom ops which are only used in the model compilation phase. Currently only single output is supported.
### Motivation and Context
Now the demand upgrade requires support for multiple outputs, so the shaper infer of ep custom op needs to be extended to support multiple outputs
---------
Co-authored-by: liumingyue <mingyue@xilinx.com>
Co-authored-by: mingyue <mingyue@amd.com>
### Description
Implement [FlashAttention](https://arxiv.org/pdf/2205.14135) and
[FlashAttention-2](https://arxiv.org/pdf/2307.08691) for
MultiHeadAttention on CPU.
### Motivation and Context
Accelerate the execution of MultiHeadAttention.
Current performance: 10ms vs 16ms (com.microsoft.MultiHeadAttention) on
my Linux machine and 10ms vs 38ms (com.microsoft.MultiHeadAttention) on
my Windows machine. May need further optimizations.
---------
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Qingnan Duan <qiduan@microsoft.com>
### Description
1. remove QNN stages from the big packaging pipeline
2. Add publish nightly package in the current [QNN Nuget
pipeline](https://dev.azure.com/aiinfra/Lotus/_builddefinitionId=1234])
### Motivation and Context
Reduce the complexity of the big Nuget packaging pipelines.
---------
Co-authored-by: Yi Zhang <your@email.com>
### Description
There are so many typos reported by the review dog, [Optional Lint]
actions (example:
https://github.com/microsoft/onnxruntime/actions/runs/9864564489/job/27239732367),
this PR is to fix some of them.
### 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: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
[DirectML] Broadcast NC-dims for Tensors A&B in DynamicQuantizeMatMul
The DynamicQuantizeMatMul allows input tensors in NCHW format, and
DirectML requires that input tensors share the same batch and channel
dimensions. Tensors A and B should be broadcast (if possible) to the
corresponding output NC dims.
### Motivation and Context
Certain models which use DynamicQuantizeMatMul hit a crash when the NC
dims are intended to be broadcast.
---------
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
Update AArch64 SQNBitGemm CompInt8 kernels to process matrix in tiles. E.g., computing the output in 2x2 tiles allows us to compute four elements of the output with one read of two rows of A and two columns of B.
Also moved some code around as it was getting big for a single file.
### Description
Our macOS pipeline are failing because of a build error in absl.
However, the bug fix we need is not available in the latest ABSL
release.
Here is the issue: https://github.com/abseil/abseil-cpp/pull/1536
And here is the fix:
779a3565ac
GTests uses ABSL. But this ABSL target also depends on GTest. So, it is
a circular dependency. We should be able to avoid that by avoid building
tests for ABSL. However, the version we are using has a problem with
that: it has cmake target that still depends on GTest even when testing
is disabled.
It's strange that we suddenly hit this problem and it only happens on macOS.
### Description
- Adds support for int4 quantized weights (per-tensor and per-channel)
on QNN EP
- Adds test script that creates an INT4 qdq model with a Conv
- Adds a unit tests demonstrating accuracy issues.
### Motivation and Context
This is the next step in being able to run models that use 4-bit
quantized weights on QNN EP.
### Description
This PR resolves a bug related to setting the **interOpNumThreads**
session option when creating an **ORTSession**. Currently, when the
**interOpNumThreads** option is passed from React Native, the native
module incorrectly sets **intraOpNumThreads** instead of
**interOpNumThreads**.
### Motivation and Context
Since this is a bug, users of the Onnx React Native package may believe
that they are setting **interOpNumThreads** correctly, So this change is
required. Refer to the code snippet below for details
<img width="634" alt="Screenshot 2024-07-05 at 9 28 58 PM"
src="https://github.com/microsoft/onnxruntime/assets/88655321/70a8f216-553a-4f4c-9481-e6871f0e37e6">
### Description
CMake logic fixed to allow enabling MPI while NCCL is disabled.
### Motivation and Context
MPI is also used on the CPU backend, not only with CUDA, so it makes
sense to decouple it properly from NCCL (which is for dealing with
multiple Nvidia GPUs).
### Description
Previously ROCMExecutionProvider uses `hipMemGetInfo` to obtain the
sizes of total memory and available memory. However, this API has been
broken since ROCm 5.7. In this PR, we use `rocm_smi` library instead of
`hipMemGetInfo`.
### Motivation and Context
`hipMemGetInfo` API has been broken since ROCm 5.7 and inference with
ROCMExecutionProvider will lead to following errors:
```
HIP failure 1: invalid argument ; GPU=0 ; hostname=4cc4900475fe ; file=/onnxruntime/onnxruntime/core/providers/rocm/rocm_execution_provider.cc ; line=229 ; expr=hipMemGetInfo(&free, &total);
```
MIOpen has a brute-force fix for this
(911e671895/src/hip/handlehip.cpp (L72)).
Instead of hard-coding available memory to 16GB, I suppose we could
obtain memory info through `rocm_smi` library as in this PR.
### Description
- Refactor codes to meet line length limit and guard missing warning
- Add slice/dropout op support
- Move vsinpu ep's cmake settings from onnxruntime_providers.cmake to a
separate file
- Modify apis with param onnxruntime::Path because this kind is replaced
by std:filesystem::path by #20920
### Description
Extend cuda minimal option to TRT provider, as with TRT 10 no linking to
cuDNN is required anymore
.
Besides that with the new engine dump feature it is also possible to
embed an engine in to an ONNX and not ship a builder lib.
In addition to that this has roughly the same deserialization
time/session setup time that using TRT standalone has.
### Motivation and Context
```
exe_builder_lib\onnxruntime_perf_test.exe -I -e tensorrt -r 5 -i 'trt_engine_cache_enable|1 trt_timing_cache_enable|1 trt_dump_ep_context_model|1 trt_weightless_engine_enable|1' model.onnx
exe_no_builder_lib\onnxruntime_perf_test.exe -I -e tensorrt -r 5 -i 'trt_engine_cache_enable|1 trt_timing_cache_enable|1 trt_dump_ep_context_model|1 trt_weightless_engine_enable|1' model_ctx.onnx
```
### Description
ETW trace logger is fakely registered as initialized_ is marked as true
before the registration is done, causing crashing issue for Lenovo
camera application.
[Bug
42610244](https://microsoft.visualstudio.com/OS/_workitems/edit/42610244):
[Watson Failure] caused by
SVCHOSTGROUP_Camera_INVALID_POINTER_READ_c0000005_onnxruntime.dll!onnxruntime::logging::Logger::Log
### Description
It might be easier if we just directly include the original gsl headers.
"core/common/gsl.h" is an indirection that doesn't provide extra help.
### Description
This PR enables the API added in #20816 as well as moving context
creation to JS.
### Motivation and Context
In order to enable I/O Binding with the upcoming
[MLBuffer](https://github.com/webmachinelearning/webnn/issues/542) API
in the WebNN specification, we need to share the same `MLContext` across
multiple sessions. This is because `MLBuffer`s are restricted to the
`MLContext` where they were created. This PR enables developers to use
the same `MLContext` across multiple sessions.
Currently WebNN TFLite backend allows the filter of
conv2d/convTranspose2d be an input. Remove the constraint and operate
necessary transpose/reshape operations for the filter input.
### Description
Support MatMulNBits shape infer in SymbolicShapeInference
MatMulNBits's B input is rank-2, so implicit merge does not apply.
### Motivation and Context
[Issue with performing shape inference using symbolic_shape_infer.py
with Phi-3 ONNX Models · Issue #21194 · microsoft/onnxruntime
(github.com)](https://github.com/microsoft/onnxruntime/issues/21194)
Fix fp8*fp8 when input A is e5m2, input B is e4m3 will run error
### 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
<!-- 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
Delete path.h and replace all occurrences of onnxruntime::Path with
std::filesystem::path.
Previously we couldn't use C++17's std::filesystem because it was not
supported in iOS 12(which was released in 2018). Now we dropped the
support for iOS 12.
### Motivation and Context
To simplify code. For example, if an EP wants to use the Path class, now
it can directly use it without going through a wrapper. And the standard
implementation can handle various path types better. (We didn't take
much consideration on UNC path, "/" as a path separator on Windows,
etc).
Add SparseAttention cpu implementation.
- [x] Refactoring GQAAttentionBase
- [x] Add SparseAttention implementation
- [x] Add test cases
This is unfused version. Flash attention version will be added later.
### Description
Make current ROCm packaging stages to a single workflow.
Reduce the possibility of all nightly packages can't be generated by one
failed stage
### Motivation and Context
Our plan is to reduce the complexity of the current zip-nuget pipeline
to improve the stability and performance of nightly packages generation.
ROCm packaging stages has no dependencies with other packaging jobs and
it's the most time-consuming route.
After this change, the most used CPU/CUDA/Mobile packaging workflow
duration can be reduced roughly from 3h20m to 2h30m.
### Problem
Currently, the codebase contains some logics pertaining to model
re-export checks and graph_builder reinitialization checks. Ideally,
these operations should function akin to a state machine. However, upon
inspecting the implementation, it becomes apparent that certain states
are checked or set in various scattered locations. This fragmentation
makes it challenging to comprehend when a re-export or re-initialization
will be triggered. For optimal clarity and maintainability, it is
advisable to consolidate these states into a cohesive component, rather
than dispersing them within the current graph execution manager.
Furthermore, the process of model exports and post-export processing for
stage 3 support or memory-efficient gradient management introduces
considerable complexity. To enhance the codebase's structure, it would
be beneficial to extract these intricate functionalities into a
dedicated component, divorcing them from the current graph execution
manager.
As part of the effort to improve the codebase, it's essential to address
inconsistencies in handling input/output flatten/unflatten operations.
Currently, there are several functions performing these operations
recursively, each with slightly different implementations. This
inconsistency leads to varying support for input/output data types and
structures in different parts of the code. To rectify this, the proposed
pull request simplifies these operations into a set of primitive
functions, ensuring uniformity. This not only streamlines the code but
also facilitates the maintenance of consistency when introducing bug
fixes or supporting new data types. One thing to mention here: input
output handling is deeply bound to the graph transition mentioned above,
so it is difficult to make this change separately.
While acknowledging the complexity of these logics, it is reassuring
that the codebase benefits from an extensive suite of unit tests that
cover all possible branches. Despite the intricacies, ensuring the
passage of all tests has been a time-intensive but necessary aspect of
this development effort.
### Design
Introduce `GraphTransitionManager` and put all model export and
post-export processing logics in it.
1. Re-export check
2. Do export
3. Re-post-export process check
4. Do post-export process
5. Return `PostExportProcessedModelInfo`, which contains all the
information we need, to pass to ORT to build gradient graph (currently
we do the same for training or evaluating, but ideally we should not do
it for evaluating, let's keep this behavior as it is now, and make the
change later).
```
# Input names for the pre-gradient-build graph.
# This may be different with the one in ExportedGraph since we may
modify the graph inputs as needed
# for example when memory efficient gradient management is enabled.
self.onnx_graph_input_names: list[str] = onnx_graph_input_names
# A subset of onnx_graph_input_names.
# Input names that require gradients for the pre-gradient-build graph.
self.onnx_graph_input_names_require_grad: list[str] =
onnx_graph_input_names_require_grad
# Create symbolic names for each dimension of the graph input (e.g.
onnx_graph_input_names).
# The key is the input name, the value is a dict of {dim_index:
symbolic_dim_name}
# e.g. {"input1": {0: "input1_dim0", 1: "input1_dim1"}, "input2": {0:
"input2_dim0"}}
self.onnx_graph_input_dynamic_axes_map: dict[str, dict[int, str]] =
onnx_graph_input_dynamic_axes_map
self.buffer_for_ort_runs: dict[str, torch.Tensor] = OrderedDict()
self.onnx_graph_input_names_user_defined = (
onnx_graph_input_names_user_defined # The ONNX graph input names
excluding the parameters, buffers.
)
# The ONNX graph input names excluding the parameters, buffers.
self.onnx_graph_input_names_require_grad_user_defined =
onnx_graph_input_names_require_grad_user_defined
self._post_export_processed_model: onnx.ModelProto | None =
post_export_processed_model
# A function to access the input data from the args and kwargs.
# If it is not None, the length is same as onnx_graph_input_names.
# For i-th input name, we can use the i-th function to get the input
data from args and kwargs.
self.data_accessor: list[callable] | None = data_accessor
# Used for unflattening the outputs from the ORT forward run.
self.module_forward_output_schema: ORTModelInputOutputSchemaType | None
= module_forward_output_schema```
The `GraphTransitionManager` instance is a property of
`GraphExecutionManager` (e.g. `TrainingManager` or ``InferenceManager),
1. Use
'self._graph_transition_manager.use_cache_or_reconstruct_post_processed_model(inputs,
kwargs)' to check whether the PyTorch module need a re-export or
re-post-export-process.
2. Use
`self._graph_transition_manager._post_export_processed_model_info.construct_inputs`
to construct the list of inputs used for ORT runs.
3. Use
`self._graph_transition_manager._post_export_processed_model_info.restore_outputs(user_outputs)`
to restore the outputs in original PyTorch output structure.
### 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
Add some macro to help print data to console for debugging purpose.
Example usage:
```
int input_id;
vector<int> some_vector;
DUMP_CPU_TENSOR_INIT()
DUMP_CPU_TENSOR("some vector", some_vector);
DUMP_STRING("input_id=", input_id);
```
- To enable dump thread id, set environment variable
`ORT_DUMP_THREAD_ID=0`.
- User can disable dumping by environment variable
`ORT_ENABLE_CPU_DUMP=0`.
### 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
It's the prerequisite step of reducing complexity of current zip-nuget
pipeline.
Some packaging tasks could be cut from the most complex nuget pipline
and easily be published
### 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
Disable using CoreML ML Program for a matmul where one of the inputs is
1D as the CoreML implementation appears to be broken. See
https://github.com/apple/coremltools/issues/2263
Add some debugging notes.
### Motivation and Context
Fix failing test on macos-14.
### Description
<!-- Describe your changes. -->
-It is an initial PR for VSINPU execution provider
### 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. -->
- For support VeriSilicon hardware
- TIM-VX(Tensor Interface Module)
(https://github.com/VeriSilicon/TIM-VX) is an integrated software
solution by Verisilicon for our hardware(A311D/i.MX 8M Plus etc.)
design, it is easy to use Verisilicon’s hardware by simply connecting
onnxruntime with the TIM-VX API by this VSINPU execution provider.
### Description
Update upstream packaging pipeline name to make it more meaningful.
### Motivation and Context
The upstream pipeline used to only building Nuget packages, but now it
also builds Zip and Java. So change the name will make it more
meaningful.
### Description
1. Update the functions in tensorprotoutils.h to use
std::filesystem::path instead of onnxruntime::Path. Eventually we can
remove the whole onnxruntime::Path class, but to this PR small I am not
doing that.
2. Remove the _SILENCE_EXPERIMENTAL_FILESYSTEM_DEPRECATION_WARNING macro
def when TensorRT EP is enabled.
### Description
Vitis AI EP synchronously supports the TensorProto data types supported
by ONNX 1.16.
Add error message show when graph resolve fail for troubleshooting.
### Motivation and Context
ONNX 1.15 & 1.16 add support some new TensorProto DataType , such as
- FLOAT8E4M3FN
- FLOAT8E4M3FNUZ
- FLOAT8E5M2
- FLOAT8E5M2FNUZ
- UINT4
- INT4
---------
Co-authored-by: liumingyue <mingyue@xilinx.com>
### Description
Provide user level options to control the fallback on CPU for models not
supported on Intel's NPU hardware.
### Motivation and Context
- Current workflow of OVEP allows safe fallback from OV NPU to OV CPU on
compilation failures. Also supports MLAS CPU fallback in presence of
unsupported custom ops.
- The PR provides a build-time option to disable fallback from OV NPU to
OV CPU.
- The session Option "kOrtSessionOptionsDisableCPUEPFallback" disables
OV CPU and MLAS CPU fallback.
- Also has bug fix for proto creation.
---------
Co-authored-by: jatinwadhwa921 <jatin.wadhwa@intel.com>
Co-authored-by: ankitm3k <ankit.maheshkar@intel.com>
### Description
1. Add QNN UTs for QNN Pad Op with FP16 data on HTP backend
2. Improve Pad op builder to handle invalid optional input
3. Add UT for ReduceSum for FP16 precision with 5D for issue reproduce
### 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. -->
