### Description
Increase the threshold to 1e-5 to avoid test failed in CUDA when
difference is slightly larger than 1e-6.
May because TF32 is used in those CUDA tests.
### Motivation and Context
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1291322&view=logs&j=f2f63060-d9d6-52d0-adee-b97db5a9ab91&t=28e21ca6-87a4-5e1e-0441-72b5e8326f2d
ProviderOptionsTest > testCUDAOptions() FAILED
org.opentest4j.AssertionFailedError: array contents differ at index
[103], expected: <0.0102678> but was: <0.010266338>
at
app//org.junit.jupiter.api.AssertionFailureBuilder.build(AssertionFailureBuilder.java:151)
at
app//org.junit.jupiter.api.AssertionFailureBuilder.buildAndThrow(AssertionFailureBuilder.java:132)
at
app//org.junit.jupiter.api.AssertArrayEquals.failArraysNotEqual(AssertArrayEquals.java:440)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:290)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:123)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:119)
at
app//org.junit.jupiter.api.Assertions.assertArrayEquals(Assertions.java:1360)
at
app//ai.onnxruntime.providers.ProviderOptionsTest.runProvider(ProviderOptionsTest.java:99)
at
app//ai.onnxruntime.providers.ProviderOptionsTest.testCUDAOptions(ProviderOptionsTest.java:43)
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1293200&view=logs&jobId=f2f63060-d9d6-52d0-adee-b97db5a9ab91&j=f2f63060-d9d6-52d0-adee-b97db5a9ab91&t=28e21ca6-87a4-5e1e-0441-72b5e8326f2d
InferenceTest > testCUDA() FAILED
org.opentest4j.AssertionFailedError: array contents differ at index
[103], expected: <0.0102678> but was: <0.010266337>
at
app//org.junit.jupiter.api.AssertionFailureBuilder.build(AssertionFailureBuilder.java:151)
at
app//org.junit.jupiter.api.AssertionFailureBuilder.buildAndThrow(AssertionFailureBuilder.java:132)
at
app//org.junit.jupiter.api.AssertArrayEquals.failArraysNotEqual(AssertArrayEquals.java:440)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:290)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:123)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:119)
at
app//org.junit.jupiter.api.Assertions.assertArrayEquals(Assertions.java:1360)
at app//ai.onnxruntime.InferenceTest.runProvider(InferenceTest.java:676)
at app//ai.onnxruntime.InferenceTest.testCUDA(InferenceTest.java:615)
### Description
<!-- Describe your changes. -->
This PR is intended to support Phi2 passes in Olive.
Merge it before https://github.com/microsoft/Olive/pull/938
### 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. -->
Adds bfloat16 as a supported dtype for SimplifiedLayerNormFusion which
will provide speedup for Llama-v2 on A100 using bfloat16 numerical
format.
_layernorm_optimized_training.onnx exported in bfloat16 vs. float16:_
### Repro Instructions
```python
from torch import nn
from onnxruntime.training.ortmodule import ORTModule, DebugOptions, LogLevel
import torch
dtype = torch.bfloat16
# dtype = torch.float16
class Net(nn.Module):
def __init__(self):
super().__init__()
self.fc = nn.Linear(784, 10, dtype=dtype)
self.layernorm = nn.LayerNorm([784], dtype=dtype)
def forward(self, x):
x = x.view(x.shape[0], -1)
x = self.layernorm(x)
x = self.fc(x)
return x
model = Net()
model = ORTModule(model, DebugOptions(save_onnx=True, onnx_prefix='layernorm', log_level=LogLevel.INFO))
model.to("cuda")
images = torch.randn((8, 28, 28), dtype=dtype).to("cuda")
output = model(images)
```
### 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. -->
ONNX Runtime integration with Llama-v2 family of LLMs.
---------
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
As per title, fixes
https://github.com/microsoft/onnxruntime/issues/19418
ONNX Runtime 1.17 broke the quantization of ONNX models with subgraphs
where initializers are placed on the top-level graph, while different
subgraphs use the same initializer.
allow protobuf-lite builds with TensorRT EP as long as it's built with
the trt built-in parser and not the oss-parser.
This is because trt built-in parser statically links protobuf so there
aren't any conflicts for protobuf-lite.
### Description
Adds a job to the python packaging pipeline that builds x64 python
wheels for QNN EP.
### Motivation and Context
Necessary to create a cached QNN model on Windows x64, which is done by
creating a properly configured onnxruntime session with QNN EP.
Enable a option to exit after session creation so that user can measure session creation time to measure impact of enabling any initialization optimizations.
Add arm64 bfloat16 fastmath mode option for transformers benchmarking script.
### Motivation and Context
onnxruntime now supports bfloat16 fastmath gemm kernels for arm64 platforms with bfloat16 instruction support. This PR updates benchmark scripts to test that mode.
### Description
Handle bugs for API backward compatability.
Update to consume the onnx model path rather the onnx serialised model
to OV compile_model API
### Description
Disable CPU EP's allocator's arena when address sanitizer is enabled,
because it masks problems. For example, the code in
onnxruntime/test/quantization/quantization_test.cc has a memory leak
problem: it allocated a buffer but didn't free it, but most memory leak
check tool cannot detect that because the buffer was from an arena and
the arena was finally freed.
### Motivation and Context
Provider better memory leak check coverage.
Otherwise, `new (BinFromIndex(b)) Bin(this, bin_size);` in bfc_arena.cc
would cause a -fsanitize=alignment (part of -fsanitize=undefined)
failure like
runtime error: constructor call on misaligned address 0xXXX for type
'Bin', which requires 8 byte alignment
There's currently a bug in the allocation planner when reusing buffers
and more than one streams are used that make it possible (although
rarely) to reach a reference count of 0 for a buffer that is still being
used. Since DML doesn't benefit from multiple streams, disabling it is
the safest option for now.
This is a high priority issue that we need to fix for 1.17.1 since it
breaks stable diffusion. Identifying the perfect fix and fixing the
underlying issue would be too risky for a patch release, especially
given the limited time that we have.
https://github.com/microsoft/onnxruntime/issues/19480
Build from source and run the command below
Example, converting whisper-base
`
python -m onnxruntime.transformers.models.whisper.convert_to_onnx -m
openai/whisper-base --model_impl openai -e -o -w --chain_model --output
./demo`
### Description
Since TypeScript v4.7, types need to specify inside "exports" field when
it is available. This PR appends types just before each "default" (which
is required by spec to be the last item).
Fixes#19403.
### Description
Add capturestate / rundown ETW support logging for session and provider
options.
### Motivation and Context
Follow-up to #16259 and #18882
This is very useful when you have longer running ONNX sessions which
will be the case for a lot of AI workloads. That means ETW tracing may
start minutes or hours after a process & session has been established.
When a trace is captured, you would want to know the state of ONNX at
that time. The state for ONNX is session and config options so that they
show up in the trace.
Tested with xperf and ORT
xperf -start ort -on 3a26b1ff-7484-7484-7484-15261f42614d
xperf -capturestate ort 3a26b1ff-7484-7484-7484-15261f42614d <--- Run
this after session has been up for some time
xperf -stop ort -d .\ort.etl <- Trace will now also have rundown events
Also these will show if you use WPR [CaptureStateOnSave
](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/capturestateonsave)
### Description
<!-- Describe your changes. -->
Python bindings aren't supported in a minimal build. Check in build.py
so user gets a better error message.
### 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. -->
#19422
### Description
<!-- Describe your changes. -->
1. add option to export onnx compatiable with ort_vllm. This makes sure
that onnx model only leverages on paged attn from vllm. It's intended to
use internally so not mentioned in readme.
2. add details in ORT
installation(https://github.com/microsoft/onnxruntime/pull/19338#discussion_r1476906190)
### 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: wejoncy <wejoncy@163.com>
### Description
This pull request includes a small change to the
`Dockerfile.manylinux2_28_cuda` file in the
`tools/ci_build/github/linux/docker` directory. The change corrects the
`PREPEND_PATH` argument from `/usr/local/cuda/binet` to
`/usr/local/cuda/bin`, ensuring the correct path to CUDA binaries is
set.
### Description
<!-- Describe your changes. -->
An overridable initializer should not have a fixed value included in an
NNAPI model as it could be changed at runtime. The current check doesn't
include validating that the initializer is constant.
I was updating GetClipMinMax as part of adding CoreML EP ML Program
support, and in order to make both CoreML and NNAPI do the more correct
thing of using IsConstantInitializer this set of changes was required.
### 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. -->
Make NNAPI and CoreML EPs more correct.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
This change
55a669409a
didn't take into account external data when unpacking initializer, and
therefore crashes when trying to unpack them.
When configured using the following CMake ops Clion is not able to
configure due to checking with `nvcc ... --dryrun tmp.cu`:
```
cmake -G Ninja -Donnxruntime_USE_TENSORRT="ON" -Donnxruntime_USE_CUDA="ON" -Donnxruntime_USE_CUDA_NHWC_OPS="ON" -DCMAKE_CUDA_ARCHITECTURES="native" -Donnxruntime_NVCC_THREADS=1 -Donnxruntime_ENABLE_NVTX_PROFILE="ON" -Donnxruntime_USE_TENSORRT_BUILTIN_PARSER="ON" -DCMAKE_CUDA_COMPILER_LAUNCHER="ccache" -Donnxruntime_BUILD_UNIT_TESTS="ON" -Donnxruntime_USE_TRITON_KERNEL=OFF -Donnxruntime_USE_FLASH_ATTENTION=OFF
```
Without building the unittests everything works fine. I believe my
changes only follow the logic that is actually desired. If
`NVCC_HAS_STRICT_ALIASING` is set to false it should not be possible to
add this as a CUDA flag. Same is true for `HAS_NOERROR` as seen in
`adjust_global_compile_flags.cmake`
[TF32](https://blogs.nvidia.com/blog/tensorfloat-32-precision-format/)
could help boost performance on GPU of SM >= 80. Sometime, user observes accuracy loss, or need disable TF32 for testing
purpose. To disable TF32, it is also possible to set environment
variable `NVIDIA_TF32_OVERRIDE = 0`. However, sometime we do not want to
use environment variable to avoid impacting other applications, or want
to have finer control (like one session using TF32, and another session
not). This provider option could help.
Here we add a provider option `use_tf32`. When `use_tf32 = 0`, we will
disable TF32 for float MatMul/GEMM in cublas. It applies to MatMulNBits,
Attention, LongformerAttention, PackedAttention,
PackedMultiHeadAttention operators when float GEMM is used internally in
the operator. Note that it will not impact other data type, like fp8
gemm could still use TF32 in accumulation.
Previously, cublasGemmStridedBatchedHelper does not use TF32 in
inference. Here we enabled TF32 by default, so we might observe speed up
for FP32 transformers models on SM >= 80.
There is another PR that enables the option for cuDNN Conv later.
### 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/15407https://github.com/microsoft/onnxruntime/issues/19288
This pull request replaces `CUBLAS_TENSOR_OP_MATH` with
`CUBLAS_DEFAULT_MATH`. The changes affect several files, including test
cases and a Python script for AMD hipify process.
### Motivation and Context
CUBLAS_TENSOR_OP_MATH mode is deprecated:
https://docs.nvidia.com/cuda/cublas/index.html#cublasmath-t
On CUDA versions prior to 11, users are required to set the math mode to
CUBLAS_TENSOR_OP_MATH manually to be able to use tensor cores for FP16.
On CUDA 11 and CUDA 12, this is no longer required. Since latest ORT
only supports CUDA >= 11 so it is safe to remove CUBLAS_TENSOR_OP_MATH
from our code base.
Bumps [github/issue-labeler](https://github.com/github/issue-labeler)
from 3.3 to 3.4.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/github/issue-labeler/releases">github/issue-labeler's
releases</a>.</em></p>
<blockquote>
<h2>v3.4</h2>
<h2>What's Changed</h2>
<ul>
<li>Fix warning by update node version by <a
href="https://github.com/renanfranca"><code>@renanfranca</code></a> in
<a
href="https://redirect.github.com/github/issue-labeler/pull/82">github/issue-labeler#82</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a
href="https://github.com/renanfranca"><code>@renanfranca</code></a>
made their first contribution in <a
href="https://redirect.github.com/github/issue-labeler/pull/82">github/issue-labeler#82</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/github/issue-labeler/compare/v3.3...v3.4">https://github.com/github/issue-labeler/compare/v3.3...v3.4</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="c1b0f9f52a"><code>c1b0f9f</code></a>
v3.4 release</li>
<li><a
href="50f02baa95"><code>50f02ba</code></a>
Fix warning by update node version (<a
href="https://redirect.github.com/github/issue-labeler/issues/82">#82</a>)</li>
<li><a
href="a8d4f1b8e8"><code>a8d4f1b</code></a>
Update README.md</li>
<li>See full diff in <a
href="https://github.com/github/issue-labeler/compare/v3.3...v3.4">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 show <dependency name> ignore conditions` will show all
of the ignore conditions of the specified dependency
- `@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)
</details>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
### Description
Changed the type annotation of sess_options in InferenceSession's
`__init__` method
### Motivation and Context
sess_options is one `SessionOptions`, not a sequence of it.
It is passed directly into `C.InferenceSession`, and from the definition
of
[`C.InferenceSession`](efc17e79de/onnxruntime/python/onnxruntime_pybind_state.cc (L1790)),
we can see that it is not a sequence:
```cpp
py::class_<PyInferenceSession>(m, "InferenceSession", R"pbdoc(This is the main class used to run a model.)pbdoc")
// In Python3, a Python bytes object will be passed to C++ functions that accept std::string or char*
// without any conversion. So this init method can be used for model file path (string) and model content (bytes)
.def(py::init([](const PySessionOptions& so, const std::string arg, bool is_arg_file_name,
bool load_config_from_model = false) {
```
When I test a new provider option, the training pipeline failed. I found
that training uses hash code of provider info to try get provider
instance. If a provider option is not used in hashing, the provider
instance fetched from cache might have different configuration for that
option.
Here I fix the hashing to use all provider options (except the default
Arena config that cannot be set from python API since training is used
with PyTorch in most cases).
Fixed a few obvious typo in the touched files.
Add regression test cases.
This pull request includes modifications to the `c-api-cpu.yml` Azure
Pipelines configuration file. The changes mainly revolve around the
Node.js packaging stage and the handling of Node.js artifacts. The most
significant changes include renaming the Node.js packaging stage, adding
a new dependency to the stage, changing artifact names, adding a new
script to list Node.js artifacts, and updating the source folder for
copying NuGet binaries.
Changes in Node.js packaging:
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L503-R508):
Renamed the Node.js packaging stage from `Nodejs_Packaging_CPU` to
`Nodejs_Packaging` and added `Windows_CI_GPU_DML_Dev` as a new
dependency to the stage.
Changes in handling of Node.js artifacts:
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L568-R569):
Changed the artifact name from `drop-onnxruntime-nodejs-win-x64` to
`drop-onnxruntime-nodejs-win-x64-dml` in the task to download pipeline
artifacts for Windows x64.
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59R595-R598):
Added a new script to list Node.js artifacts from the directory
`$(Build.BinariesDirectory)/nodejs-artifacts/win32/x64/`.
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L635-R640):
Updated the source folder from
`$(Build.BinariesDirectory)\RelWithDebInfo\RelWithDebInfo\nuget-artifacts\onnxruntime-win-x64\lib`
to `$(Build.BinariesDirectory)\nodejs-artifacts\win32\x64` in the task
to copy NuGet binaries to the directory
`$(Build.SourcesDirectory)\js\node\bin\napi-v3\win32\x64`.
---------
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
### Description
<!-- Describe your changes. -->
Add ATen fallback support for bicubic interpolation algorithm.
### 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. -->
Required for facebook/dinov2 model architecture as part of ONNX Runtime
integration with AML Vision models.
### Description
<!-- Describe your changes. -->
This PR adds
onnx conversion script for dynamo exported phi2,
optimization script,
and inference example script
A readme file is added as documentation.
https://github.com/microsoft/onnxruntime/tree/wangye/phi2_doc/onnxruntime/python/tools/transformers/models/phi2#readme
### 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>
Bumps
[gradle/gradle-build-action](https://github.com/gradle/gradle-build-action)
from 2 to 3.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/gradle/gradle-build-action/releases">gradle/gradle-build-action's
releases</a>.</em></p>
<blockquote>
<h2>v3.0.0-rc.1</h2>
<p>First release candidate of
<code>gradle/gradle-build-action@v3.0.0</code>.
This release candidate will the first release available under the
<code>v3</code> version tag.</p>
<blockquote>
<p>[!IMPORTANT]
As of <code>v3</code> this action has been superceded by
<code>gradle/actions/setup-gradle</code>.
Any workflow that uses <code>gradle/gradle-build-action@v3</code> will
transparently delegate to
<code>gradle/actions/setup-gradle@v3</code>.</p>
<p>Users are encouraged to update their workflows, replacing:</p>
<pre><code>uses: gradle/gradle-build-action@v3
</code></pre>
<p>with</p>
<pre><code>uses: gradle/actions/setup-gradle@v3
</code></pre>
<p>See the <a
href="https://github.com/gradle/actions/tree/main/setup-gradle">setup-gradle
documentation</a> for up-to-date documentation for
<code>gradle/actons/setup-gradle</code>.</p>
</blockquote>
<h2>Changes from <code>gradle-build-action@v2</code></h2>
<p>This release brings some useful and much requested features,
including:</p>
<ul>
<li>save and restore the Gradle configuration-cache data</li>
<li>add the Job summary content as a PR comment</li>
<li>easily publish Build Scans® to the free <a
href="https://scans.gradle.com">Gradle Build Scan service</a></li>
<li>compatibility with Node 20</li>
</ul>
<p>The only major breaking change from
<code>gradle-build-action@v2.12.0</code> is the update to require a Node
20 runtime environment.
Aside from that change, this release should generally serve as a drop-in
replacement for <code>gradle-build-action@v2</code>.</p>
<h3>Changelog</h3>
<ul>
<li>[NEW] - Run with NodeJs 20.x (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/946">gradle/gradle-build-action#946</a>)</li>
<li>[NEW] - Support for save & restore of configuration-cache data
(<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/966">gradle/gradle-build-action#966</a>)</li>
<li>[NEW] - Support for automatic adding PR comment with Job Summary
content (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/1020">gradle/gradle-build-action#1020</a>)</li>
<li>[NEW] - Make it easy to publish a Build Scan® to <a
href="https://scans.gradle.com">https://scans.gradle.com</a> (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/1044">gradle/gradle-build-action#1044</a>)</li>
<li>[NEW] - Added <code>dependency-graph-continue-on-failure</code>
input, which can be set to <code>false</code> to force the Job to fail
when dependency graph submission fails (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/1036">gradle/gradle-build-action#1036</a>).
Failure modes include:
<ul>
<li>Fail build step if version of Gradle being executed is not supported
for dependency-graph generation (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/1034">gradle/gradle-build-action#1034</a>)</li>
<li>Fail job if permissions are insufficient to submit dependency graph
via Dependency Submission API (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/997">gradle/gradle-build-action#997</a>)</li>
</ul>
</li>
<li>[NEW] - Add <code>dependency-graph: clear</code> option to clear any
dependency-graph previously submitted by the job</li>
<li>[FIX] Allow cache entries to be reused by jobs with the same ID in
different workflows (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/1017">gradle/gradle-build-action#1017</a>)
<ul>
<li>Workflow name remains part of the cache key, but cache entries
generated by the same job id in a different workflow may be
restored</li>
</ul>
</li>
<li>[FIX] Register pre-installed JDKs in Maven toolchains.xml file (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/1024">gradle/gradle-build-action#1024</a>)
<ul>
<li>This allows pre-installed JDKs to be auto-detected by Gradle
Toolchain support on Windows</li>
</ul>
</li>
<li>[FIX] - Update the Gradle Enterprise injection configuration for
product rename to Develocity (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/995">gradle/gradle-build-action#995</a>)</li>
<li>[FIX] - Avoid submitting an empty dependency graph when state is
loaded from configuration-cache</li>
<li>[DEPRECATION] - Deprecation of the arguments parameter (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/996">gradle/gradle-build-action#996</a>)</li>
<li>[BREAKING CHANGE] - Remove the <code>gradle-executable</code> input
parameter. Use a separate workflow Step to execute a Gradle from a
custom location.</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="4a8703fa34"><code>4a8703f</code></a>
Delegate to 'setup-gradle@v3.0.0-rc.1'</li>
<li><a
href="4a39eedb8c"><code>4a39eed</code></a>
Mention setup-gradle in README</li>
<li><a
href="272883a7ba"><code>272883a</code></a>
Remove all action sources: these have been migrated to
'gradle/actions'</li>
<li><a
href="2a8bfcf231"><code>2a8bfcf</code></a>
Delegate action implementation to gradle/actions/setup-gradle</li>
<li><a
href="e1ada08a9a"><code>e1ada08</code></a>
Bump the github-actions group with 1 update (<a
href="https://redirect.github.com/gradle/gradle-build-action/issues/1047">#1047</a>)</li>
<li><a
href="a8e3e5e2b4"><code>a8e3e5e</code></a>
Apply dependency version updates</li>
<li><a
href="2be01ca1c6"><code>2be01ca</code></a>
Build outputs</li>
<li><a
href="a00827eebb"><code>a00827e</code></a>
Bump the npm-dependencies group with 7 updates</li>
<li><a
href="ad80850e98"><code>ad80850</code></a>
Bump the github-actions group with 2 updates</li>
<li><a
href="bd6d0a74d4"><code>bd6d0a7</code></a>
Configure explicit java version for config-cache test</li>
<li>Additional commits viewable in <a
href="https://github.com/gradle/gradle-build-action/compare/v2...v3">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 show <dependency name> ignore conditions` will show all
of the ignore conditions of the specified dependency
- `@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)
</details>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
### Description
1. make parity_check use local model to avoid using hf token
2. del the model didn't work because it tried to del the object define
out of the function scope.
So it caused out of memory in A10.
3. In fact, 16G GPU memory (one T4) is enough. But the conversion
process always be killed in T4 and it works on A10/24G.
Standard_NC4as_T4_v3 has 28G CPU memory
Standard_NV36ads_A10_v5 has 440G memory.
It looks that the model conversion needs very huge memory.
### Motivation and Context
Last time, I came across some issues in convert_to_onnx.py so I use the
onnx model in https://github.com/microsoft/Llama-2-Onnx for testing.
Now, these issues could be fixed. So I use onnx model generated by this
repo and the CI can cover the model conversion.
Fix pytest version to 7.4.4, higher version will cause error
`from onnxruntime.capi import onnxruntime_validation
ModuleNotFoundError: No module named 'onnxruntime.capi'`
### Description
<!-- Describe your changes. -->
Setup usage of coremltools via dependencies instead of copying files.
Pull in some changes from
https://github.com/microsoft/onnxruntime/pull/19347 in preparation for
supporting ML Program and enabling building the ML Model on all
platforms to make development and testing of CoreML EP code easier.
- Update to coremltools 7.1
- Add patch for changes required for cross platform build of ML Program
related code
- Generate coreml proto files on all platforms
- mainly to test these changes work everywhere, as the proto files will
be used on all platforms when #19347 is checked in
- rename onnxruntime_coreml_proto target to coreml_proto as it contains
purely coreml protobuf code with no ORT related chagnes
### 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. -->
Improve setup.
### Description
This PR 1) adds LeakyRelu activation for fusedConv; 2) makes `vec4<f16>`
value work with `float32` uniforms attributes.
For example:
`clamp(value, vec4<f16>(uniforms.clip_min),
vec4<f16>(uniforms.clip_max)` will throw compilation errors since
`uniforms.clip_min` and `uniforms.clip_min` are `f32` not `f16`. So we
need to change it to `clamp(value, vec4<f16>(f16(uniforms.clip_min)),
vec4<f16>(f16(uniforms.clip_max))`
And above problem was introduced when we make activation attributes as
uniforms instead of constant.
BTW, after adding LeakyRelu, `realesrgan-t256` model can pass.
### Description
support external data in npm test.
This allows test runner to detect whether an external data is available
in the test folder, and if it is, load it as external data
automatically.
this feature does not parse every model to figure out whether the model
has external data. the following comments in code explained how to
determine whether should parse the model file.
```js
// for performance consideration, we do not parse every model. when we think it's likely to have external
// data, we will parse it. We think it's "likely" when one of the following conditions is met:
// 1. any file in the same folder has the similar file name as the model file
// (e.g., model file is "model_abc.onnx", and there is a file "model_abc.pb" or "model_abc.onnx.data")
// 2. the file size is larger than 1GB
```
