### Description
This PR is a preview of cherry-picks for ort-web to `rel-1.17.3` based
on `rel-1.17.2`.
<details>
<summary>Changes of ort-web to cherry-pick</summary>
The following commits are from main branch.
`o` stands for pick, and `x` stands for skip.
```
o 2e0a388c36 [js/webgpu] Add HardSigmoid support (#19215)
o d226e40856 [js/webgpu] set query type in onRunStart (#19202)
o 61610ff986 [js/webgpu] Add FusedConv clip test case (#18900)
o a33b5bd1fa [JS/WebGPU] Added Uniforms to SkipLayerNorm. (#18788)
o 591f90c0b9 [js/webgpu] Fix issue of timestamp query (#19258)
o 7252c6e747 [WebNN EP] Support WebNN async API with Asyncify (#19145)
o 5b06505073 [js/webgpu] Fix Tanh explosion (#19201)
o 656ca66186 [js/webgpu] Support uniforms for conv, conv transpose, conv grouped (#18753)
o a3f0e2422b [js/webgpu] Support f16 uniform (#19098)
o 9e69606360 fix f16 for attention, enable slice and flatten for more types (#19262)
o 624b4e2063 [js/webgpu] Remove enableShapesUniforms (#19279)
o 90883a366a [js/webgpu] Add hardSigmoid activation for fusedConv (#19233)
o 85cef0af8c [js/webgpu] Support capture and replay for jsep (#18989)
o d73131cf0f [js/webgpu] Use DataType as uniform cpu type (#19281)
o dd1f6ccc45 [js/webgpu] resolve codescan alert (#19343)
o 3a2ab1963a [js/webgpu] Refactor createTensorShapeVariables (#18883)
o efc17e79de [js/webgpu] Fix the undefined push error (#19366)
x 50806a7dd5 [js/web] support external data in npm test (#19377)
o ccbe264a39 [js/webgpu] Add LeakyRelu activation for fusedConv (#19369)
o 5ff27ef02a [js/webgpu] support customop FastGelu (#19392)
x 03be65e064 [js/web] fix types exports in package.json (#19458)
o 06269a3952 [js/webgpu] allow uint8 tensors for webgpu (#19545)
o dfeda9019c [JS/WebGPU] Add MatMulNBits (#19446)
o 1b48054e1b [js/webgpu] Create Split indices helpers by rank, not by shape (#19554)
o 3fe2c137ee [js] small fix to workaround formatter (#19400)
x 70567a4b3a [js/web] use ApiTensor insteadof onnxjs Tensor in TensorResultValidator (#19358)
o 6e04e36e3f [js/common] upgrade tsc in common from 4.9.5 to 5.2.2 (#19317)
o 58f4921686 [js] changes to allow Float16Array if any polyfill is available (#19305)
o 57d6819212 [js/web] Fix fused-conv is not included in npm test (#19581)
o ebd220b073 Misspelling in README.md (#19433)
o 38c3432393 Bump ip from 1.1.8 to 1.1.9 in /js/react_native (#19582)
o fe82fccf1a [js/webgpu] Fix Conv2DTransposeMatMul f16 compilation failure (#19596)
o 76a2a487a1 Bump ip from 1.1.8 to 1.1.9 in /js/react_native/e2e (#19583)
o 29b1106033 [node] Switch to setImmediate to avoid starving the Node.js event loop (#19610)
o ae3d73c981 [JS/WebGPU] Fix Split and Where to handle corner cases. (#19613)
o aec2389ad0 [js/webgpu] allows a ProgramInfo's RunData to use zero sized output (#19614)
o bb43a0f133 [js/webgpu] minor fixes to make tinyllama work (#19564)
o 0edb035808 [js/web] fix suite test list for zero sized tensor (#19638)
o 3cb81cdde2 [js/common] move 'env.wasm.trace' to 'env.trace' (#19617)
o e30618d055 [js/webgpu] use Headless for webgpu test by default (#19702)
o f06164ef8b [js/web] transfer input buffer back to caller thread (#19677)
x a788514027 [js/web] dump debug logs for karma for diagnose purpose (#19785)
o 24b72d2613 [JS/WebGPU] Preserve zero size input tensor dims. (#19737)
o 4538d31a8b [js/webgpu] expose a few properties in WebGPU API (#19857)
o 53de2d8cb0 [js/webgpu] Enable GroupedConvVectorize path (#19791)
o ed250b88c3 [JS/WebGPU] Optimize MatMulNBits (#19852)
x e771a763c3 [js/test] align web test runner flags with ort.env (#19790)
o 79e50aeef3 [js/web] rewrite backend resolve to allow multiple EPs (#19735)
o acb0df2280Fix#19931 broken Get Started link of "ONNX Runtime JavaScript API" page (#19932)
o b29849a287 [js/common] fix typedoc warnings (#19933)
o afdab62f53 Bump follow-redirects from 1.15.4 to 1.15.6 in /js/web (#19949)
o 28ad6c3955 Bump follow-redirects from 1.15.4 to 1.15.6 in /js/node (#19951)
o 7e0d424934 accumulate in fp32 for Reduce* (#19868)
o 4c6a6a37f7 [js/webgpu] Fix NAN caused by un-initialized buffer in instance-norm (#19387)
o 01c7aaf6aa [js/webgpu] allow setting env.webgpu.adapter (#19940)
o c45cff60cf [js/webgpu] fix maxpool / fp16 (#19981)
```
</details>
<details>
<summary>Cherry-pick commandlines</summary>
```sh
git cherry-pick 2e0a388c36
git cherry-pick d226e40856
git cherry-pick 61610ff986
git cherry-pick a33b5bd1fa
git cherry-pick 591f90c0b9
git cherry-pick 7252c6e747
git cherry-pick 5b06505073
git cherry-pick 656ca66186
git cherry-pick a3f0e2422b
git cherry-pick 9e69606360
git cherry-pick 624b4e2063
git cherry-pick 90883a366a
git cherry-pick 85cef0af8c #<<<<< Note: conflicts
git cherry-pick d73131cf0f
git cherry-pick dd1f6ccc45
git cherry-pick 3a2ab1963a
git cherry-pick efc17e79de
git cherry-pick ccbe264a39
git cherry-pick 5ff27ef02a
git cherry-pick 06269a3952
git cherry-pick dfeda9019c
git cherry-pick 1b48054e1b
git cherry-pick 3fe2c137ee
git cherry-pick 6e04e36e3f
git cherry-pick 58f4921686
git cherry-pick 57d6819212
git cherry-pick ebd220b073
git cherry-pick 38c3432393
git cherry-pick fe82fccf1a
git cherry-pick 76a2a487a1
git cherry-pick 29b1106033
git cherry-pick ae3d73c981
git cherry-pick aec2389ad0
git cherry-pick bb43a0f133
git cherry-pick 0edb035808
git cherry-pick 3cb81cdde2
git cherry-pick e30618d055
git cherry-pick f06164ef8b
git cherry-pick 24b72d2613
git cherry-pick 4538d31a8b
git cherry-pick 53de2d8cb0
git cherry-pick ed250b88c3
git cherry-pick 79e50aeef3
git cherry-pick acb0df2280
git cherry-pick b29849a287
git cherry-pick afdab62f53
git cherry-pick 28ad6c3955
git cherry-pick 7e0d424934
git cherry-pick 4c6a6a37f7
git cherry-pick 01c7aaf6aa
git cherry-pick c45cff60cf
```
</details>
<details>
<summary>Cherry-pick conflicts</summary>
- 85cef0af8c#18989
this change is for enabling graph capture feature for JSEP, and it is
done after ROCM EP enabled graph capture feature. However, the ROCM EP
graph capture feature is not cherry-picked in rel-1.17.2.
</details>
---------
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: Jiajia Qin <jiajia.qin@intel.com>
Co-authored-by: Xu Xing <xing.xu@intel.com>
Co-authored-by: satyajandhyala <satya.k.jandhyala@gmail.com>
Co-authored-by: Yang Gu <yang.gu@intel.com>
Co-authored-by: Wanming Lin <wanming.lin@intel.com>
Co-authored-by: Jiajie Hu <jiajie.hu@intel.com>
Co-authored-by: Guenther Schmuelling <guschmue@microsoft.com>
Co-authored-by: Matttttt <18152455+martholomew@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Segev Finer <segev208@gmail.com>
Co-authored-by: Belem Zhang <belem.zhang@intel.com>
### Description
This PR adds `BatchNormalization` with `float` support.
Some Todos:
1. all inputs don't have same data type. For example, x/y is float16,
but bias/scale is float32 or double.
2. training mode support.
We see many models are using `BatchNormalization` ops. However, due to
the missing in jsep, all of them run on cpu, which result very poor
performance. With this PR's support, densenet-9 model becomes 20.29 ms
from 250.69 ms.
### Description
This is a narrow implementation of Attention/MultiHeadAttention as it
does not support:
a. inputs 5-7 for MHA
b. packed QKV/KV
c. past/present
d. attention mask
But it works well for StableDiffusion and can be extended later. It
reduces VRAM usage as it combines many ops into few
I've updated demo here https://islamov.ai/stable-diffusion-webgpu/ it
takes ~13sec for 1 image with 20 steps on RTX3090Ti and about 25s on M1
Pro
VRAM usage is about 8gb if you don't use img2img
Going to focus on SDXL now
---------
Co-authored-by: Guenther Schmuelling <guschmue@microsoft.com>
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
### Description
<!-- Describe your changes. -->
Update XNNPACK to latest version
- adds fp16 kernels and various other improvements
- requires pthreadpool update as well
Most code updates in the XNNPACK EP are to adjust to the new XNNPACK API
- 'setup' is split into 'reshape' and 'setup'
- some ops use a workspace buffer
- copied workspace allocation from XNNPACK unit test code
- some suffixes changed
Added wrapper for XNNPACK caches to base XNNPACK EP kernel
- simplifies usage
- XNNPACK split out the code and weights caches, but the code cache
isn't currently usable via the public API
- we could use the internal types if we think it's required for
performance reasons. non-trivial though as we'd need to propagate ifdef
values from the XNNPACK build up to the ORT build.
- using XNNPACK internals would also mean we would not be able to
support using a pre-build XNNPACK package
- not an issue currently
Fixed opset registration for internal NHWC domain
- was not being tied to the ONNX version, so nodes inserted by layout
transformation had the incorrect opset
- a number of other places needed updating once this issue was fixed
Remove support for NCHW Resize from XNNPACK EP so it's NHWC only
- we only supported NCHW for fp32,
- doing so adds complexity in multiple places (XNNPACK EP kernel
implementation, layout transformation and transpose optimization)
- unclear if that complexity provides any benefit. can add back if
required by production scenario
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
We're looking at enabling fp16 support for CoreML and NNAPI. If we do
that we need a good fallback story if the CPU EP will be used. The
XNNPACK fp16 kernels will hopefully provide that.
NOTE: This PR doesn't add fp16 support to the XNNPACK EP kernels. That
can be done as required in separate EPs and should be relatively simple
to do.
### Description
Added FusedConv and FusedConvTranspose
### 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
Supported type: float. int32_t, uint32_t, bool.
Case where_broadcast.jsonc is not enabled due to
https://github.com/microsoft/onnxruntime/issues/17405.
### 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. -->
---------
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
### Description
Two contrib kernels that supposed to speed-up StableDiffusion according
to this doc
https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/stable_diffusion/README.md
However, there is no noticable effect in speed or memory consumption. So
i guess the only way to make it faster is to implement
MultiHeadAttention but i'm not capable of doing that right now. So i'll
focus on existing PRs and finding the JSEP kernel that produces
incorrect results. It should be one of the old ones (i suspect Conv or
ConvTranspose), as SD was not generating images correctly on webgpu
since i started working on it. I hoped someone else would fix that by
the time i finish with kernels/optimizations 😅
---------
Co-authored-by: Guenther Schmuelling <guschmue@microsoft.com>
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
The patch also introduces the method which copies
data from GPU to CPU synchronously.
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
Added Einsum operator support 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
<!-- 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 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
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
<!-- Describe your changes. -->
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
### Description
Added JSEP Gemm registration for opset 13. It was falling back to CPU
provider as CPU has it for 13
---------
Co-authored-by: Guenther Schmuelling <guschmue@microsoft.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
<!-- 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
Fix some Resize failing tests.
### 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
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
### 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
Added Resize NHWC domain kernel registration.
### 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
Implemented Resize operator support in 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
Added Gelu 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
Added Flatten operator support 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
Added Slice operator support 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
Added Expand operator support.
### 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
Add ConvTranspose support for WebGPU
### 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
Added WeGPU/JSEP Split operator support.
### 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
Add Concat operator
### 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
Added support for ReduceL1, ReduceL2, ReduceMean, ReduceMin, ReduceMax,
ReduceSum, ReduceLogSum, ReduceLogSumExp, ReduceProd and
ReduceSquareSum.
### 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: Satya Jandhyala <sajandhy@microsoft.com>
Co-authored-by: guschmue <guschmue@microsoft.com>
### 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.
### 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#Squeeze)
and allows one or two inputs.
### How was it tested
1. I created two models. Without `axes`:
```Python
import onnx.helper
node = onnx.helper.make_node(
"Squeeze",
inputs=["T"],
outputs=["y"],
)
graph = onnx.helper.make_graph([node], "test", [onnx.helper.make_tensor_value_info("T", 1, [3, 1, 4, 5])],
[onnx.helper.make_tensor_value_info("y", 1, [3, 4, 5])])
onnx.save(onnx.helper.make_model(graph), "squeeze.onnx")
```
And with `axes`:
```Python
import onnx.helper
node = onnx.helper.make_node(
"Squeeze",
inputs=["T", "axes"],
outputs=["y"],
)
graph = onnx.helper.make_graph([node], "test", [onnx.helper.make_tensor_value_info("T", 1, [3, 1, 4, 5]), onnx.helper.make_tensor_value_info("axes", 7, [1])], [onnx.helper.make_tensor_value_info("y", 1, [3, 4, 5])])
onnx.save(onnx.helper.make_model(graph), "squeeze-dim.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('squeeze-dim.onnx', {executionProviders: ['webgpu']});
console.log(session);
const input = new ort.Tensor('float32', new Float32Array(60), [3, 1, 4, 5]);
const dim = new ort.Tensor('int64', [-3n], [1]);
const output = await session.run({ "T": input, "axes": dim });
console.log(output);
}
run();
</script>
</html>
```
### Motivation and Context
Improve operator coverage for WebGPU JSEP.
