### Description
```
# npm audit report
socket.io 3.0.0 - 4.6.2
Severity: high
socket.io has an unhandled 'error' event - https://github.com/advisories/GHSA-25hc-qcg6-38wj
Depends on vulnerable versions of engine.io
fix available via `npm audit fix`
node_modules/socket.io
ws 8.0.0 - 8.17.0
Severity: high
ws affected by a DoS when handling a request with many HTTP headers - https://github.com/advisories/GHSA-3h5v-q93c-6h6q
fix available via `npm audit fix`
node_modules/ws
engine.io 0.7.8 - 0.7.9 || 6.0.0 - 6.5.4
Depends on vulnerable versions of ws
node_modules/engine.io
socket.io-adapter 2.5.2 - 2.5.4
Depends on vulnerable versions of ws
node_modules/socket.io-adapter
4 high severity vulnerabilities
```
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
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
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
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
<!-- 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. -->
WebNN CPU implementation has been migrated from XNNPack to TFLite which
supports more ops. Turn on partial `cpu` supported ops which just need
the change from `false` to `true` firstly.
### Description
skip default `locateFile()` when dynamic import is disabled. This allows
the file to work with bundlers to load WebAssembly file correctly if
`env.wasm.wasmPaths` is not set.
### Description
ESM: use the bundled target as default export
In this change, the default import of the following entries:
```
import from 'onnxruntime-web';
import from 'onnxruntime-web/all';
import from 'onnxruntime-web/webgpu';
```
will use the "bundled" version, which has no dynamic import.
This change should only apply to ESM on web.
# Description
This PR removes the building of the ORT "mobile" packages and much of the associated infrastructure which is no longer needed.
Not removed yet - tools/ci_build/github/android/mobile_package.required_operators.config and the helper scripts that depend on it.
# Motivation and Context
The mobile packages were deprecated in 1.18. Users should use the full packages (Android - onnxruntime-android, iOS - onnxruntime-c/onnxruntime-objc) instead or do a custom build.
Following constraints have been supported by WebNN TFLite backend:
- Concat: supports up to 4 inputs
- Matmul: supports broadcasting
- Resize: supports nearest mode
- Split: supports up to 4 outputs
To align with Office and other MS products.
Office's support policy is:
"Office for iPad and iPhone is supported on the two most recent versions
of iOS and iPadOS. When a new version of iOS or iPadOS is released, the
Office Operating System requirement becomes the two most recent
versions: the new version of iOS or iPadOS and the previous version."
(from https://products.office.com/office-system-requirements)
The latest iOS version is 17. So they support both 17 and 16. Here I set
our min iOS version to 13 so that it will be a superset of what Office
supports.
This change would allow us using C++17's std::filesystem feature in the
core framework. The modifications were generated by running
```bash
find . -type f -exec sed -i "s/apple_deploy_target[ =]12.0/apple_deploy_target=13.0/g" {} \;
```
Cannot use 15.0 because otherwise iOS packaging would fail with:
```
/Users/runner/work/1/b/apple_framework/intermediates/iphoneos_arm64/Release/_deps/coremltools-src/mlmodel/src/MILBlob/Util/Span.hpp:288:9: error: cannot use 'throw' with exceptions disabled
MILVerifyIsTrue(index < Size(), std::range_error, "index out of bounds");
```
The Google OSS libraries we use only officially support iOS 15+.
### Description
<!-- Describe your changes. -->
This PR allows to build ORT web to `ort{.all|.webgpu}.bundle.min.mjs`,
which does not have any dynamic import. This makes it possible to use
ort web via static import in service worker.
Fixes#20876
### 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
Remove explicitly concatinating pastKey with Key and pastValue with
Value.
### 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. -->
WebNN CPU backend implementation has been migrated from XNNPack to
TFLite, currently TFLite has not supported WebNN's convTranspose2d yet,
just disable it for now.
### 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 make numbers of optimizations to onnxruntime-web's module export
and deployment.
See each section below for more details.
#### Preview
>
[onnxruntime-web@1.19.0-esmtest.20240513-a16cd2bd21](https://www.npmjs.com/package/onnxruntime-web/v/1.19.0-esmtest.20240513-a16cd2bd21)
> ~~onnxruntime-web@1.19.0-esmtest.20240430-c7edbcc63d~~
> ~~onnxruntime-web@1.18.0-esmtest.20240428-624c681c83~~
> ~~onnxruntime-web@1.18.0-esmtest.20240411-1abb64e894~~
<details>
<summary><h4>Breaking changes</h4></summary>
There is no code change required, but there are a few differences
regarding **code import**, **flags**, **bundler config** and
**deployment steps**.
#### Importing:
Import table is changed. See following for details.
<details>
<summary><h5>Current import table:</h5></summary>
| Target Name | Path for "import" or "require" | WebGL | JSEP | wasm |
Proxy | Training |
|------|-----|-----|-----|-----|-----|-----|
| `ort` (default) | `onnxruntime-web` | ✔️ | ❌ | ✔️ | ✔️ | ❌ |
| `ort.all` | `onnxruntime-web/experimental` | ✔️ | ✔️ | ✔️ | ✔️ | ❌ |
| `ort.node` | `onnxruntime-web` | ❌ | ❌ | ✔️ | ❌ | ❌ |
| `ort.training` | `onnxruntime-web/training` | ❌ | ❌ | ✔️ |
✔️<sup>\[1]</sup> | ✔️ |
| `ort.wasm` | `onnxruntime-web/wasm` | ❌ | ❌ | ✔️ | ✔️ | ❌ |
| `ort.wasm-core` | `onnxruntime-web/wasm-core` | ❌ | ❌ | ✔️ | ❌ | ❌ |
| `ort.webgl` | `onnxruntime-web/webgl` | ✔️ | ❌ | ❌ | ✔️<sup>\[2]</sup>
| ❌ |
| `ort.webgpu` | `onnxruntime-web/webgpu` | ❌ | ✔️ | ✔️ | ✔️ | ❌ |
* [1] didn't test. may not actually work.
* [2] not working. this is a mistake in build config.
</details>
<details>
<summary><h5>Proposed update:</h5></summary>
| Target Name | Path for "import" or "require" | WebGL | JSEP | wasm |
Proxy | Training |
|------|-----|-----|-----|-----|-----|-----|
| `ort` (default) | `onnxruntime-web` | ✔️ | ❌ | ✔️ | ✔️ | ❌ |
| `ort.all` |
~~`onnxruntime-web/experimental`~~<br/>`onnxruntime-web/all` | ✔️ | ✔️ |
✔️ | ✔️ | ❌ |
| `ort.node` | `onnxruntime-web` | ❌ | ❌ | ✔️ | ❌ | ❌ |
| `ort.training` | `onnxruntime-web/training` | ❌ | ❌ | ✔️ | ✔️ | ✔️ |
| `ort.wasm` | `onnxruntime-web/wasm` | ❌ | ❌ | ✔️ | ✔️ | ❌ |
| ~~`ort.wasm-core`~~ | ~~`onnxruntime-web/wasm-core`~~ | ~~❌~~ | ~~❌~~
| ~~✔️~~ | ~~❌~~ | ~~❌~~ |
| `ort.webgl` | `onnxruntime-web/webgl` | ✔️ | ❌ | ❌ | ~~✔️~~ ❌ | ❌ |
| `ort.webgpu` | `onnxruntime-web/webgpu` | ❌ | ✔️ | ✔️ | ✔️ | ❌ |
</details>
#### Flags:
The following flags are deprecated:
- `env.wasm.simd` (boolean): will be ignored. SIMD is always enabled in
build.
The following flags changed their type:
- `env.wasm.wasmPaths`: When using this flag as a string ( for the URL
prefix ), nothing is changed. When using this flag as an object ( for
per-file path override ), the type changed:
```diff
- export interface Old_WasmFilePaths{
- 'ort-wasm.wasm'?: string;
- 'ort-wasm-threaded.wasm'?: string;
- 'ort-wasm-simd.wasm'?: string;
- 'ort-training-wasm-simd.wasm'?: string;
- 'ort-wasm-simd-threaded.wasm'?: string;
- };
+ export interface New_WasmFilePaths {
+ /**
+ * Specify the override path for the main .wasm file.
+ *
+ * This path should be an absolute path.
+ *
+ * If not modified, the filename of the .wasm file is:
+ * - `ort-wasm-simd-threaded.wasm` for default build
+ * - `ort-wasm-simd-threaded.jsep.wasm` for JSEP build (with WebGPU and
WebNN)
+ * - `ort-training-wasm-simd-threaded.wasm` for training build
+ */
+ wasm?: URL|string;
+ /**
+ * Specify the override path for the main .mjs file.
+ *
+ * This path should be an absolute path.
+ *
+ * If not modified, the filename of the .mjs file is:
+ * - `ort-wasm-simd-threaded.mjs` for default build
+ * - `ort-wasm-simd-threaded.jsep.mjs` for JSEP build (with WebGPU and
WebNN)
+ * - `ort-training-wasm-simd-threaded.mjs` for training build
+ */
+ mjs?: URL|string;
+ }
```
#### Bundler compatibility:
Config changes are need for bundlers. See usage example in
/js/web/test/e2e/ for Webpack, parcel and rollup.
#### Deployment:
- if consuming from a CDN, there is no breaking change.
- if consuming from a local server, need to copy all `ort-*.wasm` and
`ort-*.mjs` files (totally 6 files) in the dist folder. (previously only
need to copy `ort-*.wasm` files.)
</details>
<details>
<summary><h4>Problems</h4></summary>
There are a few problems with the current module export and deployment:
- Script URL cannot be correctly inferred when imported as ESM.
- Workers are forcefully encoded using Blob URL, which makes
onnxruntime-web not working in CSP environment and Node.js, when using
proxy or multi-threading feature.
- Generated JS code (by Emscripten) is encoded using
`function.toString()`, which is unstable and error-prone.
- When running with a different Emscripten build, always need the build
step. Making it difficult to swap artifacts in deveopment/debug.
</details>
<details>
<summary><h4>Goals</h4></summary>
- Full ESM support
- Support variances of ways to import. Including:
- import from HTML's `<script>` tag (IIFE format, exporting to global
variable `ort`)
```html
<script
src="https://example.com/cdn-path-to-onnxruntime-web/dist/ort.min.js"></script>
```
- import from source code inside `<script type="module">` tag (ESM)
```html
<script type="module">
import * as ort from
"https://example.com/cdn-path-to-onnxruntime-web/dist/ort.min.mjs";
// using 'ort'
</script>
```
- import in a CommonJS project (CJS format, resolve from package.json
"exports" field)
```js
// myProject/main.js
const ort = require('onnxruntime-web');
```
- import in an ESM project (ESM format, resolve from package.json
"exports" field)
```js
// myProject/main.js (or main.mjs)
import * as ort from 'onnxruntime-web';
```
- Support popular bundlers when importing onnxruntime-web into a CJS/ESM
project.
- webpack (esm requires extra post-process step)
- rollup
- parcel (esm requires extra post-process step)
- More bundlers **TBD**
- Multi-threading support for Node.js
NOTE: keeping single JavaScript file (the all-in-one bundle) is no
longer a goal. This is because technically there is a conflict with the
other requirements.
</details>
<details>
<summary><h4>Important Design Decisions</h4></summary>
- Drop support of single JavaScript output.
- The current onnxruntime-web distribution uses a single JavaScript file
to include all code. While there are a few benefits, it also creates
problems as mentioned above. Since ESM is being used more and more
widely, and browsers are making more restricted security checks and
requirement, the old Blob based solution is going to be replaced.
- To achieve the requirement, specifically, the CSP environment support,
we have to offer a non Blob based solution. Therefore, we have to
distribute multiple files and drop the single file solution.
- Do not run parser/postprocess on Emscripten generated JavaScript.
- Emscripten is evolving quickly so we should only depends on what's in
its documentation instead of a certain implementation details. (for
example, currently we patch on its code to deal with a special variable
`_scriptDir`)
- Keep the generated files as-is also helps to:
- reduce the size of ort.min.js
- make it easier to replace build artifacts when in development/debug
- Drop support for non-SIMD and non-MultiThread. This helps to reduce
the number of artifacts in distribution.
- (fixed-sized) SIMD is supported in any mainstream JS environment.
- Multi-thread as WebAssembly feature is supported in any mainstream JS
environment. In some environment the feature is guarded with cross
origin policy, but it can still work if not trying to create any worker.
- Use ESM output for Emscripten generated JavaScript.
- There are 2 ways to dynamically import classic (umd) modules and
neither of them are recommended:
- dynamically creating a <script> tag. This changes the HTML structure
and have quite a lot of compatibility issue
- use `fetch()` and `eval()`. However `eval` is strongly suggested to be
avoid because there is a great perf hit.
- importing ESM is super easy - just use the `import()` call.
Considering ESM is widely supported in modern browsers and Node.js this
is the better option.
- Add Blob based solution as a fallback for cross-origin workers.
- There are still wide use case of importing onnxruntime-web from CDN.
In this usage, make it able create worker by using `fetch()`+`Blob` to
create a same-origin Blob URL.
</details>
<details>
<summary><h4>Distribution File Manifest</h4></summary>
The distribution folder contains the following files:
- WebAssembly artifacts. These files are the result of compiling the
ONNX Runtime C++ code to WebAssembly by Emscripten.
| File Name | Build Flags |
|------|-----|
| ort-wasm-simd-threaded.mjs <br/> ort-wasm-simd-threaded.wasm |
`--enable_wasm_simd` <br/> `--enable_wasm_threads` |
| ort-training-wasm-simd-threaded.mjs <br/>
ort-training-wasm-simd-threaded.wasm | `--enable_training_apis` <br/>
`--enable_wasm_simd` <br/> `--enable_wasm_threads` |
| ort-wasm-simd-threaded.jsep.mjs <br/> ort-wasm-simd-threaded.jsep.wasm
| `--enable_wasm_simd` <br/> `--enable_wasm_threads` <br/> `--use_jsep`
<br/> `--use_webnn` |
- onnxruntime-web JavaScript artifacts. These files are generated by
ESBuild as the entry point for onnxruntime-web.
There are multiple build targets for different use cases:
| Target Name | Path for "import" or "require" | Description |
|------|-----|-----|
| `ort` | `onnxruntime-web` | The default target. |
| `ort.all` | `onnxruntime-web/all` | The target including webgl. |
| `ort.node` | `onnxruntime-web` | The default target for Node.js. |
| `ort.training` | `onnxruntime-web/training` | The target including
training APIs |
| `ort.wasm` | `onnxruntime-web/wasm` | The target including only
WebAssembly (CPU) EP |
| `ort.webgl` | `onnxruntime-web/webgl` | The target including only
WebGL EP |
For each target, there are multiple files generated:
| File Name | Description |
|------|-----|
| [target].js | The entry point for the target. IIFE and CommonJS
format. |
| [target].mjs | The entry point for the target. ESM format. |
| [target].min.js <br/> [target].min.js.map | The entry point for the
target. Minimized with sourcemap. IIFE and CommonJS format. |
| [target].min.mjs <br/> [target].min.mjs.map | The entry point for the
target. Minimized with sourcemap. ESM format. |
| [target].proxy.mjs | (if appliable) The proxy ESM module for the
target. |
| [target].proxy.min.mjs <br/> [target].proxy.min.mjs.map | (if
appliable) The proxy ESM module for the target. Minimized with
sourcemap. |
</details>
<details>
<summary><h4>Dynamic Import Explained</h4></summary>
- Local Served | No Proxy:
```
[Bundle or ort.min.js]
|
+ import()--> [ort-wasm-simd-threaded.mjs]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
|
+ new Worker()--> [ort-wasm-simd-threaded.mjs (worker)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
```
- Local Served | Proxy:
```
[Bundle or ort.min.js]
|
+ import()--> [ort.proxy.min.mjs]
|
+ new Worker()--> [ort.proxy.min.mjs (worker)]
|
+ import()--> [ort-wasm-simd-threaded.mjs]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
|
+ new Worker()--> [ort-wasm-simd-threaded.mjs (worker)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
```
- Cross Origin | No Proxy:
```
[Bundle or ort.min.js]
|
+ fetch('ort-wasm-simd-threaded.mjs')
|
+ URL.createObjectURL(res.blob())
|
+ import()--> [blob:... (ort-wasm-simd-threaded)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
|
+ new Worker()--> [blob:... (ort-wasm-simd-threaded) (worker)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
```
- Cross Origin | Proxy
```
[Bundle or ort.min.js]
|
+ fetch('ort.proxy.min.mjs')
|
+ URL.createObjectURL(res.blob())
|
+ import()--> [blob:... (ort.proxy)]
|
+ new Worker()--> [blob:... (ort.proxy) (worker)]
|
+ fetch('ort-wasm-simd-threaded.mjs')
|
+ URL.createObjectURL(res.blob())
|
+ import()--> [blob:... (ort-wasm-simd-threaded)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
|
+ new Worker()--> [blob:... (ort-wasm-simd-threaded) (worker)]
|
+ WebAssembly.instantiateStreaming()--> [ort-wasm-simd-threaded.wasm]
```
</details>
### Description
<!-- Describe your changes. -->
- Fix `logSeverityLevel`
- Correct get RCTCxxBridge, old method for some cases will got wrong
bridge
### 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: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
### Description
Add support for using Onnx Runtime with Node
### Motivation and Context
Onnx Runtime supports the QNN HTP, but does not support it for Node.js.
This adds baseline support for the Onnx Runtime to be used with Node.
Note it does not update the node packages that are distributed
officially. This simply patches the onnxruntime.dll to allow 'qnn' to be
used as an execution provider.
Testing was done using the existing onnxruntime-node package. The
`onnxruntime.dll` and `onnxruntime_binding.node` were swapped into
`node_modules\onnxruntime-node\bin\napi-v3\win32\arm64` with the newly
built version, then the various QNN dlls and .so files were placed next
to the onnxruntime.dll. Testing was performed on a variety of models and
applications, but the easiest test is to modify the [node quickstart
example](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/js/quick-start_onnxruntime-node).
Bump up version in main from 1.18.0 to 1.19.0 since the release branch
has been cut.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Distribute writing-to-output work over all threads in MatMulNBits.
### 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
Perform computation in fp32 and convert finally to fp16.
### 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 Key and Value inputs could be 4-dims
### 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
Fixed pastkey, key and pastvalue, value concatenation condition and
fixed index error. Added new test cases.
### 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
As described in latest discussion in #19915, parcel v2 without using the
[new resolver](https://parceljs.org/blog/v2-9-0/#new-resolver) will not
work correctly with onnxruntime-web. There are still users who uses
parcel with default resolver, so add this deprecated field "browser"
back for backward compatibility. This PR also corrects the "main" field,
which is for old resolver for Node.js.
### Description
Enabled more usecases
### 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 test runner with optional input/output.
This change fixes the OP test runner (.jsonc format test) with optional
input(s) and/or output(s).
this fix reveals a problem of dealing with optional outputs:
> Take SkipSimplifiedLayerNorm as example:
>
> if in the ONNX model, the node's outputs are: [ 'output_0', '' ]
instead of [ 'output_0' ], the current implementation will fail. The
difference is, in the first case, context.outputCount == 2, and then the
typescript implementation will try to create a tensor for output[1]. It
will eventually call to C++ function (OpKernelContext::Output), and the
output.DataRaw() will be nullptr. WebGPU backend will fail because it
cannot deal with a TensorView with data == 0.
>
This problem may need to be fixed or workaround in separated PR. This PR
does not fix this problem. Failed test cases are modified to work -
please note this PR does not break those test cases as they never work.
### Description
Currently we try to include all prebuilt binaries into the NPM packages.
This was working until we added libonnxruntime_providers_cuda.so
(>400MB) into the NPM package. The NPM registry refuses to accept new
package publishment because the file is too large.
To make the new NPM package working, we have to remove the large file
from the package, and add a new script on package installation. This
script will try to dynamically install onnxruntime CUDA dynamic library
for Linux/x64.
### Description
<!-- Describe your changes. -->
Improve performance using shared memory
### 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 with ONNX 1.16.0 branch according to
https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md
ONNX 1.16.0 release notes:
https://github.com/onnx/onnx/releases/tag/v1.16.0
#### Updated ops for CPU EP:
- DequantizeLinear(21)
- Added int16 and uint16 support + various optimizer tests
- Missing int4 and uint4 support
- Missing block dequantization support
- QuantizeLinear(21)
- Added int16 and uint16 support + various optimizer tests
- Missing int4 and uint4 support
- Missing block quantization support
- Cast(21)
- Missing int4 and uint4 support
- CastLike(21)
- Missing int4 and uint4 support
- ConstantOfShape(21)
- Missing int4 and uint4 support
- Identity(21)
- Missing int4 and uint4 support
- If(21)
- Missing int4 and uint4 support
- Loop(21)
- Missing int4 and uint4 support
- Reshape(21)
- Missing int4 and uint4 support
- Scan(21)
- Missing int4 and uint4 support
- Shape(21)
- Missing int4 and uint4 support
- Size(21)
- Missing int4 and uint4 support
- Flatten(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Pad(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Squeeze(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Transpose(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
- Unsqueeze(21)
- Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4
support
#### Unimplemented opset 21 features/ops
- int4 and uint4 data type
- QLinearMatMul(21)
- GroupNormalization(21)
- ai.onnx.ml.TreeEnsemble(5)
### 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. -->
### Disabled tests
#### ORT Training
orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py
- test_ort_custom_ops: Potential shape inference bug for custom ops
#### Python quantization unit tests
test/onnx/python/quantization (shape inference bug)
- test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16
- test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16
- test_op_gemm.py: test_quantize_qop_gemm_s8s8
- test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same
- test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3
- test_op_matmul.py: test_quantize_matmul_u8u8_f16
- test_op_matmul.py: test_quantize_matmul_s8s8_f16
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile
- test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution
- test_op_relu.py: test_quantize_qop_relu_s8s8
#### ONNX tests
- test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a
maxpool output size bug and added this test. Enable this test when [ORT
PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged.
Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741).
- test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op
ai.onnx.ml.TreeEnsemble
- test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same
- test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same
- test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same
- test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4
yet
- test_cast_INT4_to_INT8_cpu: same
- test_cast_UINT4_to_FLOAT_cpu: same
- test_cast_UINT4_to_UINT8_cpu: same
- test_cast_INT4_to_FLOAT_cuda
- test_cast_INT4_to_INT8_cuda
- test_cast_UINT4_to_FLOAT_cuda
- test_cast_UINT4_to_UINT8_cuda
- test_constantofshape_float_ones_cuda: ConstantOfShape(21) not
implemented for cuda
- test_constantofshape_int_shape_zero_cuda: same
- test_constantofshape_int_zeros_cuda: same
- test_flatten_axis0_cuda: Flatten(21) not implemented for cuda
- test_flatten_axis1_cuda: same
- test_flatten_axis2_cuda: same
- test_flatten_axis3_cuda: same
- test_flatten_default_axis_cuda: same
- test_flatten_negative_axis1_cuda: same
- test_flatten_negative_axis2_cuda: same
- test_flatten_negative_axis3_cuda: same
- test_flatten_negative_axis4_cuda: same
- test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not
implemented in ORT yet
- test_qlinearmatmul_2D_int8_float32_cpu: same
- test_qlinearmatmul_2D_uint8_float16_cpu: same
- test_qlinearmatmul_2D_uint8_float32_cpu: same
- test_qlinearmatmul_3D_int8_float16_cpu: same
- test_qlinearmatmul_3D_int8_float32_cpu: same
- test_qlinearmatmul_3D_uint8_float16_cpu: same
- test_qlinearmatmul_3D_uint8_float32_cpu: same
- test_qlinearmatmul_2D_int8_float16_cuda: same
- test_qlinearmatmul_2D_int8_float32_cuda: same
- test_qlinearmatmul_2D_uint8_float16_cuda: same
- test_qlinearmatmul_2D_uint8_float32_cuda: same
- test_qlinearmatmul_3D_int8_float16_cuda: same
- test_qlinearmatmul_3D_int8_float32_cuda: same
- test_qlinearmatmul_3D_uint8_float16_cuda: same
- test_qlinearmatmul_3D_uint8_float32_cuda: same
- test_size_cuda: Size(21) not implemented for cuda
- test_size_example_cuda: same
- test_dequantizelinear_blocked: Missing implementation for block
dequant for DequantizeLinear(21)
- test_quantizelinear_blocked_asymmetric: Missing implementation for
block quant for QuantizeLinear(21)
- test_quantizelinear_blocked_symmetric: Missing implementation for
block quant for QuantizeLinear(21)
---------
Signed-off-by: liqunfu <liqun.fu@microsoft.com>
Signed-off-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: Ganesan Ramalingam <grama@microsoft.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com>
### Description
This PR supports
[DepthToSpace](https://onnx.ai/onnx/operators/onnx__DepthToSpace.html#depthtospace)
operator in webgpu backend.
### Test
We followed the steps described on [this
page](https://gist.github.com/fs-eire/a55b2c7e10a6864b9602c279b8b75dce)
to build, tested with the following commands, and confirmed that it
passed the Model and Op tests that already existed. (Probably, these
test cases were prepared in the past for WebGL backend)
```
~/onnxruntime/js/web>
% npm test -- suite0 -b=webgpu --wasm-number-threads=1 --debug
```
##### NOTE
I want to tell you that the main branch version failed 5 tests for the
resize_upsample_sizes_nearest operator.
Since I didn't touch this issue, those test cases still fail in my
branch as well.
Should I post an issue for this?
### Motivation and Context
Though the DepthToSpace operator plays a crucial role in
super-resolution domains, it was not supported in webgpu backend.
### How to run it locally
1. conda install ninja
2. "C:\Program Files\Microsoft Visual
Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
3. python.exe {ort_repo}\tools\ci_build\build.py --config RelWithDebInfo
--build_dir {ort_repo}\build_cuda --skip_submodule_sync --build_csharp
--update --parallel --cmake_generator "Ninja" --build_shared_lib
--enable_onnx_tests --enable_pybind --build_java --build_nodejs
--use_cuda "--cuda_home=C:\Program Files\NVIDIA GPU Computing
Toolkit\CUDA\v11.8" --enable_cuda_profiling --cmake_extra_defines
CMAKE_CUDA_ARCHITECTURES=60
4. cd build_cuda\RelWithDebInfo
5. cmake --build . j16
### Motivation and Context
In packaging pipelines, we often come across a random issue that the
building with CUDA on Windows takes too much time.
Although it has been reduced much by moving the building to the CPU
machine.
We're planning to build with Ninja instead of msbuild in Packaging
pipelines, thus, nvcc can run parallelly.
It's the first step to support it locally.
### 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
Make error friendly when isOrtFormat is undefined
(`onnxruntime.InferenceSession.create` is called with ArrayBuffer or
Uint8Array).
### Motivation and Context
I was trying to run my onnx model in WebGL EP, but it gave me the error
"Cannot read properties of null (reading 'irVersion')".
I used debugger to find that actual error is `int64 is not supported`,
but the error was invisible for me.
So I made it to show both error when isOrtFormat is undefined.
<s>I haven't written unit test yet, so I'm making it draft. (I have no
idea about how do I test this though...)</s>
[d62d942](d62d9425ba)
### Description
Sometimes the `npm test` failed with an error of "TypeError: Failed to
fetch".
I checked the callback entry of the localhost server started by karma.
When the "Failed to fetch" happens, no request is reflected on the
server side. The root cause is still not identified. However, as this
issue only happens sometimes when the browser is just launched by karma
runner, doing retry can workaround this issue for most of the time.
### Description
This PR makes a change in WebGPU backend to validate program uniforms.
It compares the uniform data that comes from the result of
`getRunData()` callback from the program info, with the `ShaderHelper`'s
maintained list of uniform variables.
Fixes a few bugs that found by this check as well.
### Description
Field "browser" is deprecated in favor of "exports". Removes the unused
field.
Some bundler may read from "browser" and generate errors. Removing this
field should let bundler to look up "exports". Fixes#19915
### Description
Update Web CI to use data dir under Agent.TempDirectory
This change fixes the random failure caused by unstable access to karma
temp directory (which is under AppData\Local\Temp) on CI pipeline
### Description
Avoid using vec4 Matmul implementation for ConvTranspose with channel-last
### 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. -->
Bumps
[follow-redirects](https://github.com/follow-redirects/follow-redirects)
from 1.15.4 to 1.15.6.
<details>
<summary>Commits</summary>
<ul>
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href="35a517c586"><code>35a517c</code></a>
Release version 1.15.6 of the npm package.</li>
<li><a
href="c4f847f851"><code>c4f847f</code></a>
Drop Proxy-Authorization across hosts.</li>
<li><a
href="8526b4a1b2"><code>8526b4a</code></a>
Use GitHub for disclosure.</li>
<li><a
href="b1677ce001"><code>b1677ce</code></a>
Release version 1.15.5 of the npm package.</li>
<li><a
href="d8914f7982"><code>d8914f7</code></a>
Preserve fragment in responseUrl.</li>
<li>See full diff in <a
href="https://github.com/follow-redirects/follow-redirects/compare/v1.15.4...v1.15.6">compare
view</a></li>
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</details>
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Bumps
[follow-redirects](https://github.com/follow-redirects/follow-redirects)
from 1.15.4 to 1.15.6.
<details>
<summary>Commits</summary>
<ul>
<li><a
href="35a517c586"><code>35a517c</code></a>
Release version 1.15.6 of the npm package.</li>
<li><a
href="c4f847f851"><code>c4f847f</code></a>
Drop Proxy-Authorization across hosts.</li>
<li><a
href="8526b4a1b2"><code>8526b4a</code></a>
Use GitHub for disclosure.</li>
<li><a
href="b1677ce001"><code>b1677ce</code></a>
Release version 1.15.5 of the npm package.</li>
<li><a
href="d8914f7982"><code>d8914f7</code></a>
Preserve fragment in responseUrl.</li>
<li>See full diff in <a
href="https://github.com/follow-redirects/follow-redirects/compare/v1.15.4...v1.15.6">compare
view</a></li>
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</details>
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### Description
Fix a few warnings in typedoc (for generating JS API):
```
[warning] The signature TrainingSession.loadParametersBuffer has an @param with name "buffer", which was not used.
[warning] NonTensorType, defined in ./lib/onnx-value.ts, is referenced by OnnxValue but not included in the documentation.
[warning] TensorFactory, defined in ./lib/tensor-factory.ts, is referenced by Tensor but not included in the documentation.
[warning] ExternalDataFileType, defined in ./lib/onnx-model.ts, is referenced by InferenceSession.SessionOptions.externalData but not included in the documentation.
[warning] TensorToDataUrlOptions, defined in ./lib/tensor-conversion.ts, is referenced by Tensor.toDataURL.toDataURL.options but not included in the documentation.
[warning] TensorToImageDataOptions, defined in ./lib/tensor-conversion.ts, is referenced by Tensor.toImageData.toImageData.options but not included in the documentation.
[warning] Failed to resolve link to "GpuBufferType" in comment for Env.WebGpuFlags.adapter.
[warning] Failed to resolve link to "GpuBufferType" in comment for Env.WebGpuFlags.device.
```
Changes highlighted:
- Merge `CoreMlExecutionProviderOption` and
`CoreMLExecutionProviderOption`. They expose 2 set of different options
for React-native and ORT nodejs binding. This should be fixed in future.
- Fix a few inconsistency of names between JSDoc and parameters
- Fix broken type links
- Exclude trace functions
### Description
Fix#19931 broken Get Started link
HTTP 404 for "Get Started" link in "ONNX Runtime JavaScript API" page
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
### Description
This PR rewrite the backend resolve logic to support specifying multiple
EPs.
#### Backend
The first version of ONNX Runtime Web actually carried some existing
code from [ONNX.js](https://github.com/microsoft/onnxjs), which includes
the "backend" concept. The original "backend" in ONNX.js is designed in
a way assuming there is only one backend from user's backend hint list
will be used. For example, in ONNX.js, if user specify a backend hint as
`['webgl', 'wasm']`, ONNX.js will first try to use WebGL backend - if it
loads successfully (the browser supports webgl), then "webgl" backend
will be used and "wasm" will be ignored; otherwise, "webgl" will be
ignored and try to load "wasm" backend.
In short: only one backend will be used when initializing a session.
#### Execution Provider
Execution Provider, or EP, in ONNX Runtime is a different concept. One
of the differences is that users are allow to specify multiple EPs, and
if one does not support a particular kernel, it can fallback to other
EP. This is a very common case when using a GPU EP in ONNX Runtime.
#### Current Status: Backend v.s. EP
Because of the history reasons mentioned above, the current status is
quite confusing. There are **real backend**s, which means it's different
implementation in code; and there are **backend hint**s, which are used
as string names for backend hint; and there are **EP**s of the ONNX
Runtime concepts.
currently there are only 2 **backend**s in our code base: The "onnxjs
backend", and the "wasm backend". The "onnxjs backend" currently only
powers backend hint "webgl", which go into the old onnx.js code path.
All other backend hints including "wasm", "cpu"(alias to wasm), "webgpu"
and "webnn" are all powered by "wasm backend".
And because ORT Web treat "backend" as an internal concept and want to
align with ONNX Runtime, so those names of backend hints are becoming EP
names.
The following table shows today's status:
| Execution Provider Name (public) / Backend Hint (internal) | Backend |
EP in ORT
| -------- | ------- | ------- |
| "wasm"/"cpu" | WasmBackend | CPU EP
| "webgl" | OnnxjsBackend | \* technically not an EP
| "webgpu" | WasmBackend | JSEP
| "webnn" | WasmBackend | WebNN EP
#### Problem
While the API allows to specify multiple EPs, the backend resolving only
allows one backend. This causes issues when user specify multiple EP
names in session options, the backend resolve behavior and EP
registration behavior is inconsistent. Specifically, in this issue:
https://github.com/microsoft/onnxruntime/issues/15796#issuecomment-1925363908:
EP list `['webgpu', 'wasm']` on a browser without WebGPU support
resolves to 'wasm' backend, but the full EP list is passed in session
options, so JSEP is still enabled, causing the runtime error.
#### Solution
Since we still need WebGL backend, we cannot totally remove the backend
register/resolve system. In this PR I made the following changes:
- initialize every backend from the EP list, instead of only do that for
the first successful one.
- for the first resolved backend, filter all EP using the exact same
backend. Remove all EPs not using this backend from session options
- for every explicitly specified EP, if it's removed, show a warning
message in console
### Description
the `npm test` flags are difficult to memorize, because they are
different to the `ort.env` flags. This change makes those flags align
with ort JS API. eg. `--wasm-enable-proxy` became `--wasm.proxy`.
Old flags are marked as deprecated except `-x` (as a shortcut of
`--wasm.numThreads`)
Vectorize met 2 failed cases in a CI bot with NVIDIA GPU, but we
couldn't repro with all the GPUs at hand, including NVIDIA GPUs. This PR
introduces GPUAdapterInfo and enables this opt on non-NVIDIA GPUs to
make the bots happy.
No obivous perf gain can be seen if we enable vectorize on NVIDIA.
However, it shows big perf improvement on Intel. On my Gen12 Intel GPU,
mobilenetv2-12 perf was improved from 11.14ms to 7.1ms.
### Description
This change exposes a few properties in `ort.env.webgpu` to resolve
feature requirement mentioned in properties in
https://github.com/microsoft/onnxruntime/pull/14579#discussion_r1519612619.
- Add `powerPreference` and `forceFallbackAdapter` in `ort.env.webgpu`,
to allow users to set the value of the properties before the first
inference session is created.
- Add readonly property `adapter` in `ort.env.webgpu` to allow users to
get the adapter instance. Now users can access `ort.env.webgpu.device`
and `ort.env.webgpu.adapter`.
@xenova @beaufortfrancois
### Description
For Concat operation, the zero-size input tensor shape need to be
preserved and, unlike non-zero tensors, the dims are not constrained to
match other input tensors' dims.
### 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 Chromium Headless for webgpu test by default. Still use normal
Chromium with window when debug=true or perfMode=true.
Use the
[`--headless=new`](https://developer.chrome.com/docs/chromium/new-headless)
mode.
### Motivation and Context
try to use a more stable way to launch npm tests to avoid a "chrome not
found" issue in pipeline, which may potentially caused by windowed
application.
### Description
Try to move 'env.wasm.trace' to 'env.trace' to make it less confusing,
because it also works in webgpu. Marked 'env.wasm.trace' as deprecated.
### Description
Fixes build break brought by #19614
Currently WebGL backend does not support zero sized tensor. This change
split test data into 2 parts, and only enable zero sized tensor tests
for WebGPU.
### Description
This PR allows zero-sized output.
To make the implementation simple, it does not support partial
zero-sized tensor. Which means, either all outputs are zero-sized, or an
error will be reported.
added 2 tests:
- op test of `Add` with input T[2,0] T[2,1], and
- test_split_zero_size_splits
### Description
<!-- Describe your changes. -->
1. Fix Where operator to handle Boolean input less than 4 bytes.
2. Fix JSEP test harness to use tensor names consistently.
### 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. -->
Switch to setImmediate to avoid starving the Node.js event loop
There should really be a true async version though, running
computationally intensive things on the event loop will stop everything
else from happening while it is running, e.g. a web server from
answering requests.
This can be done by wrapping `RunAsync` behind a
[`napi::Promise`](https://github.com/nodejs/node-addon-api/blob/main/doc/promises.md)
to run on the onnxruntime thread pool or [`AsyncWorker`](
https://github.com/nodejs/node-addon-api/blob/main/doc/async_worker.md)
for the Node.js/libuv thread pool.
### 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. -->
Without this, if you run inference in a tight loop, without anything
else in between that is async/deferred, `process.nextTick` will lead to
starving the event loop and not letting anything else run,
`setImmediate` at least lets the event loop spin between calls to `run`.
See
https://dev.to/ynmanware/setimmediate-settimeout-and-process-nexttick-3mfd
Contributed on behalf of [Swimm](https://swimm.io/)
Bumps [ip](https://github.com/indutny/node-ip) from 1.1.8 to 1.1.9.
<details>
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This is used in sam-h-decoder-f16.
### Description
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### Motivation and Context
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Bumps [ip](https://github.com/indutny/node-ip) from 1.1.8 to 1.1.9.
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BUG: https://github.com/microsoft/onnxruntime/issues/18855
### 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 change adds only necessary code to enable ort-web works with any
Float16Array polyfill. Unlike #19302, in this PR, ort-web does not
include any specific polyfill; instead, it's user's choice for how to
use a polyfill.
ORT-web uses Float16Array if it's available; otherwise, fallback to use
Uint16Array.
```js
// case 1: user does not use polyfill:
import * as ort from 'onnxruntime-web';
const myF16Data = new Uint16Array(...); // need to use Uint16Array
const myF16tensor = new ort.Tensor('float16', myF16Data, dims);
```
```js
// case 2: user use polyfill:
import * as ort from 'onnxruntime-web';
import {
Float16Array, isFloat16Array, isTypedArray,
getFloat16, setFloat16,
f16round,
} from "@petamoriken/float16";
globalThis.Float16Array = Float16Array; // ort-web will pick the global Float16Array
const myF16Data = new Float16Array(...); // Use the polyfilled Float16Array type
const myF16tensor = new ort.Tensor('float16', myF16Data, dims);
```
### Description
This is required to make shape uniforms really work.
### Motivation and Context
The bug was unveiled in a model with multiple Split nodes. The later
nodes would try to reuse a previous pipeline cache, while the old shapes
were hardcoded as constants in cache.
### Description
Add MatMulNBits to support MatMul using 4-bit quantized weights
### 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
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
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
```
### Description
This PR expands the graph capture capability to JS EP, which is similar
to #16081. But for JS EP, we don't use the CUDA Graph, instead, we
records all gpu commands and replay them, which removes most of the cpu
overhead to avoid the the situation that gpu waiting for cpu.
mobilenetv2-12 becomes 3.7ms from 6ms on NV 3090 and becomes 3.38ms from
4.58ms on Intel A770.
All limitations are similar with CUDA EP:
1. Models with control-flow ops (i.e. If, Loop and Scan ops) are not
supported.
2. Usage of graph capture is limited to models where-in all ops in the
model can be partitioned to the JS EP or CPU EP and no memory copy
between them.
3. Shapes of inputs/outputs cannot change across inference calls.
4. IObinding is required.
The usage is like below:
Method 1: specify outputs buffers explicitly.
```
const sessionOptions = {
executionProviders: [
{
name: "webgpu",
},
],
enableGraphCapture: true,
};
const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);
// prepare the inputBuffer/outputBuffer
... ...
const feeds = {
'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
};
const fetches = {
'output': ort.Tensor.fromGpuBuffer(outputBuffer, { dataType: 'float32', dims: [1, 1000] })
};
let results = await session.run(feeds, fetches); // The first run will begin to capture the graph.
// update inputBuffer content
... ...
results = = await session.run(feeds, fetches); // The 2ed run and after will directly call replay to execute the graph.
... ...
session.release();
```
Method 2: Don't specify outputs buffers explicitly. Internally, when
graph capture is enabled, it will set all outputs location to
'gpu-buffer'.
```
const sessionOptions = {
executionProviders: [
{
name: "webgpu",
},
],
enableGraphCapture: true,
};
const session = await ort.InferenceSession.create('./models/mobilenetv2-12.onnx', sessionOptions);
// prepare the inputBuffer
... ...
const feeds = {
'input': ort.Tensor.fromGpuBuffer(inputBuffer, { dataType: 'float32', dims })
};
let results = await session.run(feeds); // The first run will begin to capture the graph.
// update inputBuffer content
... ...
results = = await session.run(feeds); // The 2ed run and after will directly call replay to execute the graph.
... ...
session.release();
### 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
```math
\tanh(x)=\frac{e^x-e^{-x}}{e^x+e^{-x}}=
\left\{
\begin{array}{cc}
-\frac{1-e^{-2\cdot(-x)}}{1+e^{-2\cdot(-x)}}, & x<0 \\
0, & x=0 \\
\frac{1-e^{-2x}}{1+e^{-2x}}, & x>0
\end{array}
\right.
```
### Motivation and Context
On some platforms,
$$\tanh(1000)=\frac{e^{1000}-e^{-1000}}{e^{1000}+e^{-1000}}$$ would
produce NaN instead of 0.999... or 1 (imagine $e^{1000}=\infty$ and
$\frac{\infty}{\infty}$ explodes).
When we enable webgpu profiling mode between session.create and
session.run, current implementation has a problem to create querySet
(and also queryResolveBuffer) if we share the commandEncoder with inputs
upload. This PR fixes this by moving the querySet creation to the place
we set queryType.
### Description
Added Uniforms to SkipLayerNorm
### 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 performance
---------
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
### Description
<!-- Describe your changes. -->
`env.webgpu.profiling` is a global flag. It may change before each
session.run. So the best place is to update it in `onRunStart` event.
After this, we can directly check `this.queryType`'s value. Without this
pr, we need to make sure that `getCommandEncoder()` is called before
checking `this.queryType`. Otherwise, it may happen that
`pendingKernels`'s length is not equal to `pendingDispatchNumber`'s
length. See the two ugly workarounds
[1)](e630dbf528 (diff-006fc84d3997f96a29b8033bd2075d6a0a9509211bd5812a6b934fc74fedfd9dR267-R268))
and
[2)](e630dbf528 (diff-618fe297fbe7a1da586380163b8fd2627311ccc217640a3c5cdc9c17a33472c1R73-R80))
if we don't introduce `onRunStart`. Or we need to call `setQueryType` in
each kernel run.
### Description
This op is required in mobilenetv3-small-100. With this PR,
mobilenetv3-small-100 model becomes less than 10 ms from over 100 ms on
ADL.
### 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
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>
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.
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
when DOM API is not avaiable, using OffscreenCanvas
### Motivation and Context
In some environment like service worker or web worker, the DOM API is
not avaiable, we can use OffscreenCanvas API to replace
`document.createElement('canvas')`.
Most of the APIs of OffscreenCanvas and HTMLCanvasElement are the same,
except that `toDataUrl` is missing.
It fix this issues #19032
resize for fp16 has 2 issues: scales are always f32 and roi can be f32
or f16.
scales:
this is fixed.
roi
this is fixed for the case where roi is not passed as optional input
with f16. To fix this it requires a much larger change and I did not
want to risk this short before a release. For all practical purpose
passing roi as input with f16 should be rare and we can fix it in the
near future.
Update WebNN test list in suite-test-list.jsonc so all test cases are
passed behind WebNN CPU backend on Chrome Stable (Although some cases
may fall back to CPU EP).
Enable int64 support for WebNN in unit tests.
### Description
Change `A / sqrt(B)` to `A * inverseSqrt(B)` in BatchNormalization,
InstanceNormalization, LayerNormalization and SkipLayerNormalization.
### Motivation and Context
For the same reason as the existence of the `inverseSqrt` built-in in
WebGPU spec.
