### Description
upgrade packages version.
```
# npm audit report
electron 23.0.0-alpha.1 - 23.3.13
Severity: moderate
ASAR Integrity bypass via filetype confusion in electron - https://github.com/advisories/GHSA-7m48-wc93-9g85
fix available via `npm audit fix --force`
Will install electron@28.1.4, which is a breaking change
node_modules/electron
get-func-name <2.0.1
Severity: high
Chaijs/get-func-name vulnerable to ReDoS - https://github.com/advisories/GHSA-4q6p-r6v2-jvc5
fix available via `npm audit fix`
node_modules/get-func-name
semver <=5.7.1 || 6.0.0 - 6.3.0 || 7.0.0 - 7.5.1
Severity: moderate
semver vulnerable to Regular Expression Denial of Service - https://github.com/advisories/GHSA-c2qf-rxjj-qqgw
semver vulnerable to Regular Expression Denial of Service - https://github.com/advisories/GHSA-c2qf-rxjj-qqgw
semver vulnerable to Regular Expression Denial of Service - https://github.com/advisories/GHSA-c2qf-rxjj-qqgw
fix available via `npm audit fix`
node_modules/cross-spawn/node_modules/semver
node_modules/global-agent/node_modules/semver
node_modules/semver
```
### Description
<!-- Describe your changes. -->
1. Make JBLAS codes an external module of ORT.
2. Move q4 gemm code to contrib_ops.
3. Update template kernel library to v0.1 release.
### 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. -->
We found that the current LLM model performance is far below our
expectations. Here is some performance data collected on Mistral-7B
model with Xeon-8480:
8 threads | prompt length=32 past_len=32 | prompt length=1 past_len=32
-- | -- | --
ORT-main | 1220ms | 263ms
Neural-speed | 564ms | 87ms
ORT-this PR|597ms|120ms
Although `Neural-speed` and `ORT-this PR` use the same int4 kernel code,
there is a 33ms(87ms vs. 120ms) latency gap between the two frameworks.
Through some statistics analysis, the summary latency of `MatMulNBits`
is 86.7ms
The summary latency of all int4 GEMMs in `Neural-speed` is 84.8ms. So
other OPs introduce an extra 30ms latency.
The performance of MatMulNBits in this PR meets our expectations.
### Remain Issues
1. For hybrid CPUs, like core 12900K, the ONNXRuntime thread pool uses
TaskGranularityFactor to scale its number of threads. This is not
expected in our code design. It may slow down the hybrid CPU performance
by 30~40%.
2. Prepack uses a single thread which is very slow to init a session.
3. MatMulNBits with zero points will fall through to COMP_FP32 even
accuracy_level=4. Our COMP_INT8 IGemmCore with zero points process is
not optimized for now. It will be updated in the future. So, for an int4
model with zero points, whether the accuracy_level is 0 or 4 will be no
difference.
### 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
show warning when numThreads is set but threads is not supported.
Resolves#19148, #18933
for web: when crossOriginIsolated is false.
for node: always disable.
### Description
<!-- Describe your changes. -->
Bump up version to 1.18.0 since the release branch has been cut.
### 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: rachguo <rachguo@rachguos-Mini.attlocal.net>
### Description
Extends the code coverage to Entroy, Histogram and Distribution
calibration method, fix bugs while doing it.
### Motivation and Context
Bugs detected in [Olive](https://github.com/microsoft/OLive).
### 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. -->
Previously building webnn ep with --disable_rtti will throw
unboundTypeError since unbound type names are illegal with RTTI disabled
in Embind API, we can fix it by adding a
-DEMSCRIPTEN_HAS_UNBOUND_TYPE_NAMES=0 flag.
### 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
Update DML version to 1.13.1
### 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
Fix build error when ENABLE_NPU_ADAPTER_ENUMERATION is defined
### 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 Java `TensorInfo` object which is used to describe a tensor's shape,
along with the input and output placeholders for a model couldn't show
any symbolic/named dimensions in that tensor. Now this information is
stored in Java strings on construction and included in the toString.
### Motivation and Context
Setting symbolic dimensions required external information in Java, the
names were not discoverable from within the API.
### Description
<!-- Describe your changes. -->
### Motivation and Context
Linux_GPU_x64 job in the pipeline has been canceled due to timeout since
0112.
### Description
<!-- Describe your changes. -->
The `split` input of the Split op is int64_t. Fixing that resolves a
type mismatch build error on Windows when CoreML is enabled (for
debugging the partitioning code).
### 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. -->
Fix build error
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
The `OnnxValue` and `OrtProviderOptions` implementations now check to
see if they've been closed before accessing the native pointer, and also
before close is called.
### Motivation and Context
Before they could be closed twice which SIGSEGV'd the JVM. Fixes#19125.
### Description
This way, we will not need to update the windows images constantly and
allow more flexibility to choose the cuda version in the future.
### Description
Set default flags nvcc and do not set the flags for ROCM EP.
### Motivation and Context
1. To meet a BinSkim requirement for CUDA EP.
https://github.com/microsoft/binskim/blob/main/docs/BinSkimRules.md#rule-BA2024EnableSpectreMitigations
2. The ROCM EP's pipeline is broken since PR #19073 . Unit tests failed
to load the EP with the following error message:
Failed to load library libonnxruntime_providers_rocm.so with error:
/build/Release/libonnxruntime_providers_rocm.so: undefined symbol:
vtable for onnxruntime::InsertMaxPoolOutput .
This PR is a hot fix to bring the pipeline back. So far I don't know why
the error happened. The symbol "InsertMaxPoolOutput" is in
onnxruntime_optimizers. I don't see any EP code references it directly.
### Description
Disable ccache for all the jobs in in Windows CPU CI pipeline.
Before disabling it, the build has a warning that:
"MSIL .netmodule or module compiled with /GL found; restarting link with
/LTCG; add /LTCG to the link command line to improve linker performance"
After disabling it, the warning is gone and the build doesn't use /GL or
/LTCG.
Cache itself should not cause this difference.
### Motivation and Context
We submit kernels in a batch (a fixed number 16 is used except for the
last batch) for better performance. However, timestamp query support is
at pass level so we disable the batch execution in profiling mode in
previous implementation. Actually we can have multiple passes in a batch
so that we don't have to disable batch execution, which is the first
enhancement of this PR.
Furthermore, WebGPU has an extension to support timestamp query inside
passes, which isn't supported by all the platforms (e.g., Windows
supports it, while macOS doesn't). This is expected to have lower cost
compared with multiple passes solution. So this PR also introduce this
support when available.
This PR also refactors some implementation related to kernelInfo, and
try to unify the related kernel names.
### Description
enable external data loading for ort-web.
### Why
The ORT external data design is highly depending on the file system,
especially synchronous file I/O APIs. Those are not available in web
platforms. We need to have extra code to make external data working on
web.
### How
Considering there is no file system in web, an implementation for web to
support external data is to use pre-loaded data. Assume model file
a.onnx includes initializers that linked to ./b.bin, we require users to
pass a full data file list when creating the session. The user code will
be look like:
```js
const mySess = await ort.InferenceSession.create('./path/model/a.onnx', {
// session options
externalData: [
{
// relative or absolute path/URL of the file,
// or a pre-loaded Uint8Array containing the data of the external data file
data: './path/data/b.bin',
// the relative path of the external data. Should match initializers' "location" value defined in the model file
path: './b.bin'
},
// { } if multiple external data file
]
});
```
Currently, this feature only works with JSEP build enabled.
### 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. -->
Add new option `trt_engine_cache_prefix` to customize TRTEP engine cache
prefix.
i.e:
- If user specifies `trt_engine_cache_prefix|FRCNN
trt_engine_cache_enable|true` when running FRCNN model
- the cache will be saved/loaded:
`FRCNN_2068723788287043730_*_sm80.engine`. Engine profile follows same
pattern.
- If skipping this option, the engine will be saved/loaded:
`TensorrtExecutionProvider_TRTKernel_graph_torch-jit-export_2068723788287043730_*_*_sm80.engine`
as default case.
### 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/16708
---------
Co-authored-by: Chi Lo <Chi.Lo@microsoft.com>
Co-authored-by: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
when jsep calls javascript with an index to HEAP8 or HEAP32 the index is
negative when the heap is above 2GB, even if we pass it as uint32_t it
remains negative. So in javascript use >>> 0 to make it unsigned.
### Description
- Updates `get_qnn_qdq_config()` to use new scale/zp np.array data
types.
- Adds missing unit test to help prevent future regression.
### Motivation and Context
https://github.com/microsoft/onnxruntime/pull/18043 changed the usage of
`extra_options["TensorQuantizationOverrides"]`. We need to update its
use in quantization/execution_providers/qnn/quant_config.py
### Description
<!-- Describe your changes. -->
Pass through the ConfigOptions from the session via OpKernelInfo so that
kernel behavior can be configured.
Initial usage would be to optionally enable a fast path for ARM64 bloat16 GEMM - see #17031
Other usages could be things like selected the exact implementations of the activation functions for RNN operators instead of the default approximations (e.g. use [sigmoid_exact instead of sigmoid](2d6e2e243d/onnxruntime/core/providers/cpu/rnn/rnn_helpers.h (L379-L382)))
OpKernelInfo is already passing through things from the session state, and adding a new member of ConfigOptions
is the simpler update. It's also a more natural fit given it's providing state/info to the kernel.
### 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. -->
