### Description
Added Einsum operator support to JSEP.
### Motivation and Context
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### Description
Include Support for neg.int32
### Motivation and Context
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### Description
Commit fffefb1c22 (#16969) optimized
matmul and also fixes broadcasting. So #17191 is no longer needed.
However, the newly added operator test file from the PR by @dakenf is
helpful so pick and add it to enhance the tests.
### Description
<!-- Describe your changes. -->
### Motivation and Context
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[//]: # (## Work In Progress. Feedbacks are welcome!)
### Description
This PR adds a few properties, methods and factories to Tensor type to
support IO-binding feature. This will allow user to create tensor from
GPU/CPU bound data without a force transferring of data between CPU and
GPU.
This change is a way to resolve#15312
### Change Summary
1. Add properties to `Tensor` type:
a. `location`: indicating where the data is sitting. valid values are
`cpu`, `cpu-pinned`, `texture`, `gpu-buffer`.
b. `texture`: sit side to `data`, a readonly property of `WebGLTexture`
type. available only when `location === 'texture'`
c. `gpuBuffer`: sit side to `data`, a readonly property of `GPUBuffer`
type. available only when `location === 'gpu-buffer'`
2. Add methods to `Tensor` type (usually dealing with inference
outputs):
- async function `getData()` allows user to download data from GPU to
CPU manually.
- function `dispose()` allows user to release GPU resources manually.
3. Add factories for creating `Tensor` instances:
a. `fromTexture()` to create a WebGL texture bound tensor data
b. `fromGpuBuffer()` to create a WebGPUBuffer bound tensor data
c. `fromPinnedBuffer()` to create a tensor using a CPU pinned buffer
### Examples:
create tensors from texture and pass to inference session as inputs
```js
// when create session, specify we prefer 'image_output:0' to be stored on GPU as texture
const session = await InferenceSession.create('./my_model.onnx', {
executionProviders: [ 'webgl' ],
preferredOutputLocation: { 'image_output:0': 'texture' }
});
...
const myImageTexture = getTexture(); // user's function to get a texture
const myFeeds = { input0: Tensor.fromTexture(myImageTexture, { width: 224, height: 224 }) }; // shape [1, 224, 224, 4], RGBA format.
const results = await session.run(myFeeds);
const myOutputTexture = results['image_output:0'].texture;
```
### Description
Changes in this PR:
1) use the optimized version `makeMatMulPacked[Vec4]Source` to support
matmul.
2) enable the conv2dByMatMul path.
3) support broadcast
4) use IndicesHelper.
MatMul with M = 512, K = 512, N = 512 becomes 2ms from 15ms when
enabling profilingMode on my ADL.
### Description
This PR adds kernel implementation for operator "Not" and "Equal". Also
removed download cache in gpu data manager.
**Why removing download cache**
The following test case failed. ("Or" is on CPU, "Greater" and "Equal"
are on JSEP)
after debugging, I found that both "Equal" and "Greater" are using the
same output GPU Data ID. This is because when ORT executes the graph, it
first run "Equal", allowing its shader to write into GPU Data ID 2; then
a Gpu2Cpu copy for it is issued (because currently "Or" is on CPU EP);
at this point, ORT thinks GPU Data ID=2 is free to use; so it reuse it
as output for "Greater". This means there is no allocation for output of
"Greater" kernel, and both kernel writes to GPU Data ID=2.
For gpu data manager, there will be 2 downloads from the same GPU
buffer. Previously I think this is a waste of resource so I cached the
data. But now it shoes that we need to perform 2 downloads because the
GPU data is already different. The download data cache should be
removed.
### Motivation and Context
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### Description
<!-- Describe your changes. -->
### Motivation and Context
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### Description
Fix JSEP ConvTranspose shader code errors.
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
### Description
Enable typed binary and support int32 type for binary.
Co-authored-by: Xing Xu <xing.xu@intel.com>
---------
Co-authored-by: Xing Xu <xing.xu@intel.com>
### Description
Add SkipLayerNormalization operator to JSEP.
### 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 a typo. LayerNormalization takes 2 or 3 inputs. The third input,
bias, is optional.
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
### Description
1. allows passing session options to operator test (eg. graph
optimization level)
2. add a short flag '-x' for '--wasm-number-threads' as it is frequently
used.
### Description
<!-- Describe your changes. -->
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
### Description
test case 'test_batchnorm_epsilon_training_mode' on webgpu is failing.
the issue need time to investigate so comment this off and re-enable it
when the root cause is fixed.
### Description
Fix some Resize failing tests.
### Motivation and Context
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- 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
enable webgpu in browser unit test.
The CI pipeline uses Edge v113+ which enables WebGPU.
===
**UPDATE on 08/07/2023:**
- add flags to Edge browser launch commandline so that Edge on CI agents
can initialize WebGPU correctly.
- ONLY enable webgpu on web release build. Other pipelines are using
flag `-b=wasm,webgl,xnnpack` to specify the other 3 backends explicitly.
- disable "Resize" related test failures. Once they are fixed the tests
can be re-enabled.
---------
Co-authored-by: Satya Jandhyala <satya.k.jandhyala@gmail.com>
### Description
Added two kernels for Layer and Instance norm
Also added maximum limits for `maxBufferSize` when requesting GPU device
as by default it's limited to 256mb and it fails allocating 600mb buffer
while running fp32 StableDiffusion weights.
### Motivation and Context
These two are used in StableDiffusion and many other networks
Fixed ArgMin and ArgMax and refactored using functionality from Reduce
operator code.
### Description
Removed code/functionality duplication and fixed some issue.
### Motivation and Context
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### Description
Make CacheHint mechanism, which is designed to avoid running the same
test multiple times saving the result mapped against a key, working by
adding input dims.
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
### Description
update op test schema.
This changes fixes several problems for operator tests for web:
- `opsets` -> `opset`: an operator uses exactly one opset instead of
multiple
- `condition` -> `platformCondition`: make it less confusing
- `inputShapeDefinitions`: allows to test ORT behaviors when it get
no/partial/full shape info.
Added a JSON schema file and also an example file
### Description
Added Gather op that works with both i32 and i64 indices, assuming that
values fall into i32 limit. The assumption is safe because it's not
possible to allocate more than 2gb buffer for inputs.
It treats all data from input tensor as u32, copying 1 or 2 elements for
i64, u64 and double.
---------
Co-authored-by: Guenther Schmuelling <guschmue@microsoft.com>
argmax and argmin are similar to reduce. Eventually we need to add
optimized flavors of the shader.
softmax is optimized but only works on the last axis for now which
should be the common use case.
todo: enable more ut for argmax/argmin
### Description
Implemented Resize operator support in JSEP
### Motivation and Context
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### Description
Added Gelu operator to JSEP
### Motivation and Context
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### Description
Added Flatten operator support to JSEP.
### Motivation and Context
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### Description
Added Slice operator support to JSEP.
### Motivation and Context
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### Description
This change upgrades a lot of dependencies. There are 2 motivations of
doing this change:
- fix the security issue reported by dependabot (protobufjs Prototype
Pollution vulnerability -
https://github.com/advisories/GHSA-h755-8qp9-cq85)
- resolve the requirement of using ONNX IR_VERSION 9 (#16638)
This requires:
- upgrade protobufjs to v7.2.4
- upgrade library 'onnx-proto' to consume latest ONNX release (v1.14.0).
Problems:
- protobufjs v7.2.4 depends on long.js v5, which does not work well with
typescript (commonjs).
- onnx-proto depends on this fix with a new release of long.js
- long.js is in maintenance and it takes longer than expected to put in
new changes
Solutions:
- use a patch script in `preprepare` to copy type declarations to make
long.js work with typescript (commonjs)
- generate onnx protobuf JS/TS files and put them under
js/web/lib/onnxjs/ort-schema/protobuf folder - remove 'onnx-proto' from
dependency.
- apply fixes to generated onnx.d.ts
### Description
Added Expand operator support.
### Motivation and Context
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### Description
Add ConvTranspose support for WebGPU
### Motivation and Context
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### Description
Added WeGPU/JSEP Split operator support.
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
### Description
Add missing L1Reduce and L2Reduce operator kernels.
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
### Description
We used to use `typeof fetch === 'undefined'` as condition to detect the
environment is Node.js or not. Before Node.js v18, this works. However,
in Node.js v18, it introduced `fetch` function, so this check does not
work any more.
This PR changes the condition to check whether `process`,
`process.versions` and `process.versions.node` exists.
Checking whether `process` exists is not enough. This is because in some
configuration, webpack may polyfill nodejs's process.
### Description
Added support for ReduceL1, ReduceL2, ReduceMean, ReduceMin, ReduceMax,
ReduceSum, ReduceLogSum, ReduceLogSumExp, ReduceProd and
ReduceSquareSum.
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
---------
Co-authored-by: Satya Jandhyala <sajandhy@microsoft.com>
Co-authored-by: guschmue <guschmue@microsoft.com>
### Description
<!-- Describe your changes. -->
refactor tensor type in onnxruntime-common.
### Motivation and Context
There major motivation is that I am doing a local change to address the
API part of #15312. And I am doing a refactoring of onnxruntime-common
anyway (#15772).
The `tensor.ts` and `tensor-impl.ts` are too large, so I split contents
into multiple files to make the type declarations clearer.
The original target of this change is for API only ( ie. do not refactor
any implementation.). However, there are a few type/implementation
inconsistencies so I also made minimal changes to fix them.
### Changes
- extract `TensorUtils` for non-template interfaces
- extract `TensorFactory` for all overloads of `Tensor.fromImage()`
- refactor options type that used for `Tensor.fromImage()`
- fix JSDoc comments to make option descriptions consistent with actual
type declarations
- fix an inconsistency for `options.format` and `options.bitmapFormat`;
change all `bitmapFormat` to `format`
- extract `ConversionUtils` for `tensor.toDataURL()` and
`tensor.toImageData()`
- put implementations into multiple files from `tensor-impl.ts`
- fix a bug that cause unittest fail. put comments for future fix.
### Description
Add an API for users to get version of current package. example usage:
```js
import { env } from 'onnxruntime-node';
console.log(env.versions.node); // output "1.16.0"
```
```js
import { env } from 'onnxruntime-web';
console.log(env.versions.web); // output "1.16.0"
console.log(env.versions.common); // output "1.16.0"
console.log(env.versions.node); // output "undefined"
```
#16156
### Description
This PR adds an implementation of the Squeeze operator to WebGPU JSEP.
The implementation follows the [operator
schema](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Unsqueeze).
To implement the `Unsqueeze` operator in the same fashion as the
`Squeeze`, I added the `ComputeOutputShape()` method to the
`UnsqueezeBase` class and made some slight modifications. Please let me
know if it is a bad idea and if I should move this method to the JS
implementation.
I also uncommented test case lines in the `suite-test-list.jsonc` file
for both Squeeze and Unsqueeze operators following @hariharans29's
[comment](https://github.com/microsoft/onnxruntime/pull/16024#issuecomment-1565113633).
### How was it tested
1. I created a model with only one operator:
```Python
import onnx.helper
node = onnx.helper.make_node(
"Unsqueeze",
inputs=["T", "axes"],
outputs=["y"],
)
graph = onnx.helper.make_graph([node], "test", [onnx.helper.make_tensor_value_info("T", 1, [3, 4, 5]), onnx.helper.make_tensor_value_info("axes", 7, [2])], [onnx.helper.make_tensor_value_info("y", 1, [3, 1, 4, 5, 1])])
onnx.save(onnx.helper.make_model(graph), "unsqueeze.onnx")
```
2. I compiled the runtime using @fs-eire's
[instructions](https://gist.github.com/fs-eire/a55b2c7e10a6864b9602c279b8b75dce).
3. I ran the test models in the browser using this minimal setup:
```HTML
<html>
<script src=".\dist\ort.webgpu.min.js"></script>
<script>
async function run() {
const session = await ort.InferenceSession.create('unsqueeze.onnx', {executionProviders: ['webgpu']});
console.log(session);
const input = new ort.Tensor('float32', new Float32Array(60), [3, 4, 5]);
const dim = new ort.Tensor('int64', [1n, 4n], [2]);
const output = await session.run({ "T": input, "axes": dim });
console.log(output);
}
run();
</script>
</html>
```
### Motivation and Context
Improve operator coverage for WebGPU JSEP.
