followed the rocm example below it which isn't the naming convention we
want to follow. didn't fix rocm because i'm not sure if there are
consumers using its naming convention.
### Description
Fix random crash for QNN UTs with multi-thread run like
QnnHTPBackendTests.MultithreadHtpPowerCfgDefaultAndRunOption
Root cause, last minute code change
b4e26bd5f9
static std::mutex mutex; -> OrtMutex mutex;
missed static.
### Description
Update DML EP for `FusedMatMul` ORT graph node have TransA/B attribute
set instead of updating the strides.
### 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. -->
Use the latest nuget.exe for the `readme` property to be supported.
### 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. -->
#22137
The spec renames MLOperandDescriptor.dimensions to
MLOperandDescriptor.shape, in order to support older Chromium versions,
we will keep both in WebNN EP for a while.
Fixed#22120
### Description
<!-- Describe your changes. -->
ONNXRuntime implementation of S8S8 was using the default C++
implementation; with this new ISA, all variants of QGemm Int8 can
support VNNI dot product and full AVX2 instructions.
All signed/unsigned variants support VNNI instructions starting with
LNL.
Renamed structs and functions to better indicate support of all Int8 vs
U8X8
### 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. -->
LNL HW implemented new ISA, and this code enables that ISA in QGemm.
Speed is improved for S8S8 to match with existing U8S8 code. S8U8 would
also match speed if ONNX formally accepted the data type.
### Description
Fix regression caused by #17361
### 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
This PR refactors the `CPU` kernel for the `CumSum` operator. The new
implementation strives to have as little indirection as possible.
### Motivation and Context
Currently the `CumSum` operator perform very poorly in the case of 1D
tensors(it was slower than a python loop). This is caused by the
extensive use of the `SliceIterator`-s.
Here is a relevant snippet:
```python
import time
import ndonnx as ndx
import onnxruntime as ort
import numpy as np
import onnx
def test_cumsum(sz):
a = ndx.array(shape=(sz,), dtype=ndx.int64)
b = ndx.cumsum(a)
model = ndx.build({'a': a}, {'b': b})
onnx.save(model, "model.onnx")
input = np.ones(sz, np.int64)
start = time.time()
result = ort.InferenceSession(model.SerializeToString()).run(None, {'a': input})
end = time.time()
return end - start
def test_cumsum_by_hand(sz):
input = np.ones(sz, np.int64)
start = time.time()
answer = [0]
for i in input:
answer.append(answer[-1] + i)
end = time.time()
return end - start
print(test_cumsum(int(1e7)))
print(test_cumsum_by_hand(int(1e7)))
```
Before
```console
0.9794480800628662
0.4518160820007324
```
After
```console
0.02483987808227539
0.5496008396148682
```
The `model.onnx`:
<img width="214" alt="image"
src="https://github.com/user-attachments/assets/a213d6ff-86c3-49b5-a493-ebfd97deaa41">
The flame graph:
### Description
Update XNNPack to latest version (Sep 4)
- Some op outputs are changed, channel or stride paras are moved into
reshape func.
e.g.
96962a602d
- input params of xnnpack's resize related function are changed a lot
- KleidiAI is added as a dependency in ARM64
- The latest XNNPACK includes 2 static libs microkernels-prod and
xnnpack.
Without microkernels-prod, it throws the exception of Undefined symbols.
- Add ORT_TARGET_PROCESSOR to get the real processor target in CMake
### Description
See https://github.com/microsoft/onnxruntime-extensions/pull/476
and https://github.com/actions/runner-images/issues/7671
### 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 issue
- [ ] For default xcode 15.2, that come with the MacOS-13, We Need to
update the boost container header boost/container_hash/hash.hpp version
to pass the build
- [x] For xcode 14.2 The Build passed but the `Run React Native Detox
Android e2e Test` Failed.
Possible flaky test, https://github.com/microsoft/onnxruntime/pull/21969
- [x] For xcode 14.3.1 We encountered following issue in `Build React
Native Detox iOS e2e Tests`
```
ld: file not found: /Applications/Xcode_14.3.1.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/arc/libarclite_iphonesimulator.a
clang: error: linker command failed with exit code 1 (use -v to see invocation)
```
Applied following code to the eof in both ios/Podfile and fixed the
issue
```
post_install do |installer|
installer.generated_projects.each do |project|
project.targets.each do |target|
target.build_configurations.each do |config|
config.build_settings['IPHONEOS_DEPLOYMENT_TARGET'] = '13.0'
end
end
end
end
```
- [x] https://github.com/facebook/react-native/issues/32483
Applying changes to ios/Pofile
```
pre_install do |installer|
# Custom pre-install script or commands
puts "Running pre-install script..."
# Recommended fix for https://github.com/facebook/react-native/issues/32483
# from https://github.com/facebook/react-native/issues/32483#issuecomment-966784501
system("sed -i '' 's/typedef uint8_t clockid_t;//' \"${SRCROOT}/Pods/RCT-Folly/folly/portability/Time.h\"")
end
```
- [ ] Detox environment setting up exceeded time out of 120000ms during
iso e2e test
### dependent
- [x] https://github.com/microsoft/onnxruntime/pull/21159
---------
Co-authored-by: Changming Sun <chasun@microsoft.com>
`supportsModel` is deprecated in TRT 10.1.
Add `supportsModelV2 `but still keep `supportsModel` as we still need to
support TRT 8.6 where `supportsModelV2 ` is not
supported.
Perf test data(100000 times)
Array: 12.599999997764826ms
String: 1.6000000014901161ms
Perf test case:
```
const permFunctionBodyArray = (rank: number, input: string): string => {
const reverseFunc = [];
reverseFunc.push(`fn perm(i: int) -> int {
var a: int};`);
for (let i = 0; i < rank; ++i) {
reverseFunc.push(input);
}
reverseFunc.push('return a;}');
return reverseFunc.join('\n');
};
const permFunctionBodyString = (rank: number, input: string): string => {
let reverseFunc= `fn perm(i: int}) -> int {
var a: int;`;
for (let i = 0; i < rank; ++i) {
reverseFunc+=input;
}
reverseFunc+='return a;}';
return reverseFunc;//.join('\n');
};
const count = 100000;
let start, end
console.time('array');
start = performance.now();
for(let i =0 ; i < count; i ++) {
permFunctionBodyArray(3, 'input');
}
end = performance.now();
console.timeEnd('array');
console.log("Array: "+ (end-start));
console.time('string');
start = performance.now();
for(let i =0 ; i < count; i ++) {
permFunctionBodyString(3, 'input');
}
end = performance.now();
console.log("String: " +(end-start));
console.timeEnd('string');
```
### 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. -->
This is to fix issue #22031 to run model demucs.
For conv-transpose, outputPadding.length could be 1, while spatialRank
is 2. The fix is to append enough 0s to outputPadding. For conv, the
issue is similar. kernelShape.length sometimes could be 1, while
inputs[1].dims.length is 4. The fix is also to append enough 0s to
kernelShape.
### Description
Added checks to convert partial vectors in the early stages of the FP16
to FP32 cast using AVX NE CONVERT ISA.
### Motivation and Context
Avoid storing data in sections outside of the output buffer, these
checks are missing on the [original
PR](https://github.com/microsoft/onnxruntime/pull/21183).
This fix prevents memory corruption when the output buffer has a size
[n*16 + 1, n*16 + 7] with 0< n
patch from @john-dance
"The main change is simple: Use the original node name rather than the
original node op_type when creating new nodes. Here are my comments on
the change:
------
The onnx runtime uses the op_type as the basis for a new node name, so a
node claimed by QNN EP might be named
Conv_token_1 with no relation to the original /conv1/Conv. This patch:
1. Adds OpName as a virtual function in NodeRef and implements it in
ApiNode.
2. AddNode now takes an op_name and op_type and passes them both to
CreateNodeHelper.
3. CreateNodeHelper uses the op_name rather than the op_type in
GenerateNodeName
4. Direct calls to AddNode are modified to either use the NodeRef if
available, or just repeat the op_type if not available.
The result is that the new nodes are named something like
/conv1/Conv_token_1, allowing a straight forward mapping back to the
original model node (if they exist in the original graph)."
### Description
Adds support for constructing an `OrtSession` from a
`java.nio.ByteBuffer`. These buffers can be memory mapped from files
which means there doesn't need to be copies of the model protobuf held
in Java, reducing peak memory usage during session construction.
### Motivation and Context
Reduces memory usage on model construction by not requiring as many
copies on the Java side. Should help with #19599.
- Remove hard code data type checks and use WebNN's opSupportLimits
instead
- Add HasSupportedOutputsImpl for output data type validation
- Get preferred layout info from opSupportLimits
- Move Not op to logical_op_builder.cc because it should be there. This
avoid the inconsistent input names in `unary_op_builder.cc`.
### Description
This PR will add support for Continuous Decoding for batch_size = 1
input. From now on, GQA can take arbitrary length input using seqlens_k
as total_sequence_length - 1 and the sequence length of qkv as
new_sequence_length.
**This change will not affect the default behavior of GQA**
### Motivation and Context
Prior to this change it was impossible to support sequence_length > 1
inputs when past context was given. This use case is essential to making
continuous decoding work, which is one of our current efforts in
ORT-GenAI.
### Description
This PR makes the following updates to the Arm Compute Library execution
provider:
- Target Arm Compute Library 24.07
- Add support for the following operators:
- Conv (FP16)
- NhwcConv
- QLinearConv
- MatMul
- FusedMatMul
- MatMulIntegerToFloat
- Optimize memory usage and performance
- Expose the enable_fast_math setting
- Use the main runtime thread pool
### Motivation and Context
These updates improve performance and memory usage, and enable use of a
more recent version of Arm Compute Library.
@microsoft-github-policy-service agree company="Arm Ltd"
---------
Signed-off-by: Michael Tyler <michael.tyler@arm.com>
### Description
Fixes a bug where the buffer offset and position was incorrectly
computed if the user supplied a `ByteBuffer` to `createTensor` but set
the type of the tensor to something other than `INT8`. This would be
more common if the user was trying to load the initializers from a
serialized representation and didn't want to bother with the type
information (which is the case in #21321).
### Motivation and Context
Partial fix for #21321. The remainder of the fix is to add a helper
which allows users to load initializers out of an `onnx_data` file, but
that will require adding protobuf as a dependency for the Java API to
allow the parsing of an ONNX file separately from the native code. It
might be nicer to put that functionality into ORT's C API so it can
return the lengths & offsets of the initializers when provided with an
ONNX file containing external initializers. We hit this kind of thing in
Java more often than other languages as in Java models can be supplied
as classpath resources which we can easily read, but not materialize on
disk for the ORT native library to read.
### Description
Updates QNN EP to properly reject nodes that have inputs or outputs with
dynamic shapes.
### Motivation and Context
Currently, QNN EP does not properly offload subgraphs with dynamic
shapes to the CPU EP. This PR ensures that QNN EP rejects nodes that
consume or generate I/O with dynamic shapes.
### Description
Extend VitisAI EP `tensor_proto_as_raw` API to support memory buffer
containing the TensorProto external data
### Motivation and Context
For reduce peak memory usage, VitisAI EP need support ORT format model
and setting session option
`session.use_ort_model_bytes_for_initializers` for enable directly use
the model bytes for initializers.
Co-authored-by: mingyue <mingyue@xilinx.com>
### Description
This PR adds two new libfuzzer in fuzzer project.
1. Binary libfuzzer
2. libprotobuf-fuzzer
To compile run below cmd on linux:
```
LLVM_PROFILE_FILE="%p.profraw" CFLAGS="-g -fsanitize=address,fuzzer-no-link -shared-libasan -fprofile-instr-generate -fcoverage-mapping" CXXFLAGS="-g -shared-libasan -fsanitize=address,fuzzer-no-link -fprofile-instr-generate -fcoverage-mapping" CC=clang CXX=clang++ ./build.sh --update --build --config Debug --compile_no_warning_as_error --build_shared_lib --skip_submodule_sync --use_full_protobuf --parallel --fuzz_testing --build_dir build/
```
Run fuzzer:
```
LD_PRELOAD=$(clang -print-file-name=libclang_rt.asan-x86_64.so) build/Debug/onnxruntime_libfuzzer_fuzz testinput -rss_limit_mb=8196 -max_total_time=472800 -fork=2 -jobs=4 -workers=4 -ignore_crashes=1 -max_len=2097152 2>&1 | grep -v "\[libprotobuf ERROR"
```
### Motivation and Context
The existing custom fuzzer is not coverage guided and it's slow and it
will work on one model mutation at a time. The new fuzzers are coverage
guided, and we can use more models' files as a corpus to increase the
coverage.
### Description
<!-- Describe your changes. -->
Change the `CMAKE_CXX_COMPILER_VERSION` greater than `11` for using
'-mavxvnni'.
### 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. -->
`CMakeFiles/onnxruntime_mlas.dir/root/Git.d/onnxruntime/onnxruntime/core/mlas/lib/x86_64/QgemmU8S8KernelAvx2.S.o
cc: error: unrecognized command-line option ‘-mavxvnni’; did you mean
‘-mavx512vnni’?` using `gcc (GCC) 10.3.1`.
`-mavxnni` is supported since [GCC 11
Release](https://gcc.gnu.org/gcc-11/changes.html), this PR change the
version check.
### Description
When building with GCC 14.2.1, I got the following warning:
onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.h:329:59:
error: template-id not allowed for constructor in C++20
[-Werror=template-id-cdtor]
Remove template parameters from the constructor: The constructor
TreeAggregatorMax<InputType, ThresholdType, OutputType> has been
simplified to TreeAggregatorMax, because the compiler already knows the
template parameters from the class definition.
### Motivation and Context
Fix the build issue
Signed-off-by: Clément Péron <peron.clem@gmail.com>
### Description
<!-- Describe your changes. -->
### Motivation and Context
The parameter isn't correct.
Maybe it hasn't negative impact by chance so far.
d8e64bb529/cmake/CMakeLists.txt (L1712-L1717)
Error Codes are added to catch compilation error and signal recompile.
Remote Tensors are added to ensure direct memory access for NPU
inferencing.
UMD Bypass cache enabled with 2024.4 will eliminate need to disk caching
### Motivation and Context
The changes are needed to ensure backward compatibility
UMD Bypass caching eliminates driver caching
Remote Tensors lead to performance improvement with inferencing on NPU
---------
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: Srirammaswamy <srirammaswamy.s@intel.com>
Co-authored-by: saurabh <saurabh1.kale@intel.com>
Co-authored-by: Javier E. Martinez <javier.e.martinez@intel.com>
Co-authored-by: Eric Crawford <eric.r.crawford@intel.com>
Co-authored-by: jatinwadhwa921 <jatin.wadhwa@intel.com>
### Description
For ROCm device, the host side code needs to call GPU_WARP_SIZE_HOST to
query warpSize
of the underlying GPU device.
### Motivation and Context
Fixes MatMulNBits tests on gfx1100/01 which has warpSize of 32.
Signed-off-by: Jagadish Krishnamoorthy <jagadish.krishnamoorthy@amd.com>
### Description
unordered_map are implemented in a different way on VisualStudio and
gcc. It seems that inserting consecutive keys has a poor performance on
Windows.
### Motivation and Context
Improve the performance of onnxruntime when initializing trees.
### Description
### Motivation and Context
For some model has pattern Pad -> Conv. If the Conv doesn't have pads
attributes, the Pad can be fused into Conv.
### Description
Added CUDNN Frontend and used it for NHWC ConvTranspose op including
option for bias fusion. Similar to this [Conv
PR](https://github.com/microsoft/onnxruntime/pull/19470)
### Backward compatible
If ORT is built with cuDNN 8, cuDNN frontend will not be built into
binary. Old kernels (using cudnn backend APIs) are used.
### Major Changes
For cuDNN 9, we will enable cudnn frontend to fuse data gradient
convolution and bias when a provider option fuse_conv_bias=1.
### Potential Issues
cuDNN frontend uses TF32 by default. It can be disabled using use_tf32
cuda provider option, but in the case cuDNN frontend encounters issues
building an operation graph it will fallback to using TF32.
### Follow ups
This is one of the PRs that target to enable NHWC, here the
ConvTranspose operation in CUDA EP by default if device supports it.
There are other changes will follow up to make it possible.
(1) Enable prefer_nhwc by default for device with sm >= 70.
(2) Change fuse_conv_bias=1 by default after more testing.
(3) Add other NHWC operators (like Resize or UpSample).
### Motivation and Context
The new CUDNN Frontend library provides the functionality to fuse
operations and provides new heuristics for kernel selection. Here it
fuses the convolution data gradient operation (ConvTranspose) with the
pointwise bias operation.
### Minor Change
In the CUDA convolution operation was a small bug when
`GetCudnnConv1dPadToNc1d ` was enabled.
### Description
This PR adds the optimizer logic to fuse the newly designed exported
ONNX models for Phi-3 vision and Phi-3.5 vision.
### Motivation and Context
After the re-designed export of Phi-3 vision and Phi-3.5 vision, the
ONNX models for the vision component and embedding component contain
`If` and `Loop` ops to handle multi-image support.
