### Description
The changes in this PR includes:
1) Fix f16 errors in InstanceNormalization with NCHW format.
2) Use vec to further optimize the original algorithm.
3) (Removed) Don't do layout conversion for InstanceNormalization for
JSEP since InstanceNormalization itself is suitable for NCHW layout and
has better performance in our current implementation.
Tested on sd-vae-decoder-f16.onnx, it becomes 285 ms from 314 ms. The
aggregate gpu profiling data can be found as below (Note the data is
based change 3).):
Before:
<html>
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<!--StartFragment--><span><span class="ui-provider ef bbg bbh bbi bbj
bbk bbl bbm bbn bbo bbp bbq bbr bbs bbt bbu bbv bbw bbx bby bbz bca bcb
bcc bcd bce bcf bcg bch bci bcj bck bcl bcm bcn" dir="ltr">
Kernel | Time (Ms) | Percentage (%)
-- | -- | --
Conv | 201.55 | 69.56
InstanceNormalization | 42.49 | 14.67
Transpose | 28.95 | 9.99
Mul | 5.69 | 1.96
Add | 3.82 | 1.32
MatMul | 3.27 | 1.13
Sigmoid | 2.24 | 0.77
Resize | 1.16 | 0.40
Softmax | 0.34 | 0.12
Cast | 0.24 | 0.08
Sum | 289.75
<br class="Apple-interchange-newline"><!--EndFragment-->
</body>
</html>
After:
<html>
<body>
<!--StartFragment--><span><span class="ui-provider ef bbg bbh bbi bbj
bbk bbl bbm bbn bbo bbp bbq bbr bbs bbt bbu bbv bbw bbx bby bbz bca bcb
bcc bcd bce bcf bcg bch bci bcj bck bcl bcm bcn" dir="ltr">
Kernel | Time (Ms) | Percentage (%)
-- | -- | --
Conv | 205.44 | 79.43
InstanceNormalization | 18.24 | 7.05
Transpose | 17.64 | 6.82
Mul | 5.69 | 2.20
Add | 3.81 | 1.47
MatMul | 3.56 | 1.38
Sigmoid | 2.24 | 0.86
Resize | 1.19 | 0.46
Softmax | 0.59 | 0.23
Cast | 0.24 | 0.09
Sum | 258.65 |
</span></span><!--EndFragment-->
</body>
</html>
From above table, we can see that two ops time are greatly reduced. One
is InstanceNormalization and the other is Transpose. The reason that the
transpose time is reduced is because each InstanceNormalization is
surrounded with two reshape ops in sd-vae-decoder-f16.onnx. Due to JSEP
is prefer NHWC and InstanceNormalization is layout sensitive op, so two
extra transpose ops are inserted dynamically when executing this model.
After this change, those inserted transpose ops are not needed anymore.
So the overall transpose time is reduced.
### Description
This PR provided a vectorized matmul algorithm. In most situations, we
still go to the workgroup memory optimized matmul. But for some
situations, like N and K are very small, using workgroup optimized
matmul can't fully utilize the underlying hardware due to the 32x32 tile
size. So for very small N/K, we switch to the naive vectorized matmul
algorithm to improve the hardware execution unit usage.
With this PR, matmul with input0: [1, 36864, 3], input1: [1, 3, 3],
input2: [3] becomes less than 1 ms from 4.34 ms on Intel Gen9 GPUs.
### Description
<!-- Describe your changes. -->
Added uniforms to Reduce op
### 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 perforamnce.
### Description
<!-- Describe your changes. -->
Added uniforms to Tile and Where Ops
### 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.
### Description
* implemented lazyResetGrad function
### Motivation and Context
* we are in the process of adding language bindings to enable training
on web
* lazyresetgrad ensures that the gradients are calculated correctly
after the first runTrainStep call
---------
Co-authored-by: Ashwini Khade <askhade@microsoft.com>
### Description
**This PR is a replacement of #17820.**
allow to specify callback for profiling data
*Previous*:
```js
ort.env.webgpu.profilingMode = 'default'; // enable profiling
// profiling data will output to console.
```
*Now*:
```js
ort.env.webgpu.profiling = {
mode: 'default'; // enable profiling
ondata: (data) => {
// .. process the profiling data
}
};
//for each kernel, "ondata" will be called once. only output to console if ondata is not specified.
```
### Description
Use Uniforms in GatherElements and clean-up
### 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
### Description
* implemented runEvalStep and runOptimizerStep
* added hasEvalModel and hasOptimizerModel boolean fields in
TrainingSession representation
* added evalInputNames and evalOutputNames fields to
TrainingSessionHandler & TrainingSession
* removed the inputNamesEncoded and outputNamesEncoded fields from
TrainingSessionHandler -- since none of the training methods require the
input names and output names as parameters, there's no need to store
them.
### Motivation and Context
* part of the work for implementing web bindings for training
* previous PR: #18250
---------
Co-authored-by: Ashwini Khade <askhade@microsoft.com>
### Description
Currently, we only print the kernelName, which is hard to distinguish
which shader we actually used. For example, GroupedConv/Conv2DMatMul
both belong to Conv kernel. It's not intuitive for profiling.
With uniform support, ideally we may just keep one artifact for each
program to save the compilation time. This PR just logs the related
info, including key and program name, so that we may understand better
the situation.
### Description
Add uinforms to Einsum
### 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.
### Description
This PR includes a change that inspired from #18452 to resolve a
requirement: a shader may depend on an instance of `IndicesHelper` to
generate WGSL code snippet, but the IndicesHelper instance is not
necessarily an input/output of the program. So the existing
`declareVariables()` function does not work with this scenario.
In order to support this requirement, I added this "use" function to
`interface ShaderHelper`, which takes a helper-like object as parameter.
The hidden implementation `ShaderHelperImpl` class will iterate the
helpers and call `impl()` for each.
@axinging @qjia7
### Description
Currently, all conv2dMatmul with inChannels = 3 and outChannels % 4 = 0
will report compilation errors. Models, which include this kind of shape
will be impacted, like mobilenetv2-12, resnet50 .
The errors is introduced by #18452https://github.com/microsoft/onnxruntime/pull/18452/files#diff-8b24ea43aa11b1346c0c9e327f9bce6b37a93bd8f2bf8a6392b2b263972b7ea2R200,
which accidentally pass `components` to `x`. But `x`'s components is
`innerElementSize` not `components `. And when `innerElementSize` is 3,
we should use `1` in current design.
### Description
* Implemented: `getParametersSize`, `getContiguousParameters`
(equivalent to copyParametersToBuffer), and `loadParametersBuffer`
(equivalent to copyParametersFromBuffer)
* as part of these changes, getParametersSize was added to the
TrainingSession interface so that users know what size buffer to create
for loadParametersBuffer
* The parameters methods in the interface were modified to take in a
Float32Array instead
### Motivation and Context
* part of the work for implementing web bindings for training
* enables federated learning in the web
* previous PR: #18006
---------
Co-authored-by: Ashwini Khade <askhade@microsoft.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.
This change refactored matmul/conv related programs to support shape
uniforms. Currently only matmul shape uniforms are fully enabled.
TODOs: add input dependencies for conv related programs, turn clipMax
and clipMin to uniforms.
### Description
<!-- Describe your changes. -->
Added Uniforms to Expand operator kernel
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Improve performance
### Description
It was a mistake to use 2 different names for Clip operator in
op-resolve-rules.ts for different opset. An optimized implementation can
handle both cases (opset < 11 and opset >=11). Remove "ClipV10" as an
entry from the table.
### Description
Currently, the binary algorithms are divided into the vectorize one
(efficient) and non-vectorize one (less efficient). Below situations
will go to the vectorize one:
1) A or B's shape length is 1.
2) The shared dimensions length of A and B are divisible by 4.
3) A and B have same shape.
This PR adds another situation as below to go to the vectorize
algorithm.
4. A or B's last dimension is divisible by 4.
With this change, the aggerate time of Add in sam-b-encoder becomes
309.65 ms from 409.12 ms on Intel ADL.
### Description
optimize eslint config to:
- set parserOptions.project to `true` to allow @typescript-eslint/parser
to find the nearest tsconfig.json file to that source file. This helps
to avoid parsing extra files, may helps with:
- reduce the possibility of seeing OOM or stackoverflow with "npm run
lint"
- faster processing
- enforce rule "no-underscore-dangle" with a list of exceptions.
### 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
Support uniforms in Slice op
### 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 ferformance
### Description
- set tsconfig "noUnusedParameters" to `true` and fix a few bugs
discovered by typescript.
how unused parameter is fixed:
- for most code (webgl), add underscore as prefix, which is the standard
ignore pattern for typescript check.
- remove unused parameter from function and modify corresponding
function calls (jsep)
- fix a bug in ArgMinMax: this 2 operators do not have more than one
input(s) so the `createArgMinMaxAttributesFromInputs()` is removed.
- add proxy main.ts into typescript check and fix a bug in parameter
passing
- fixed `run()` function call and add typecheck fix (hack)
### Description
This PR fixes the TypeScript type check.
Previously, when I use esbuild to replace webpack (#17745), typescript
typecheck was disabled. This causes a few TypeScript type error checked
in into the code base. This PR fixes the followings:
- Use "Node16" as default "module" value in tsconfig.json, because in
TypeScript v5, `(module == "ES2015" && moduleResolution == "Node16")` is
an invalid combination.
- Set `noUnusedParameters` to true as default. in web override it to
false because multiple code need to be updated ( a following-up PR will
do this )
- set correct project file for 'web/lib/**/*.ts' for ESLint (otherwise
WebGPU types are not populated correctly)
- fix type error in file js/web/lib/wasm/jsep/webgpu/program-manager.ts
- upgrade "@webgpu/types" to latest to fix type error in file
js/web/lib/wasm/jsep/backend-webgpu.ts
- add package script "prebuild" for web to run tsc type check
- add type check in CI yml file
### Description
For Resize, when `noScale` is true, the shader can become very simple,
which is not related with `attributes.mode` anymore. So we should remove
those parts of shader code for simplification.
This PR can also fix#18311 since the `noScale` are all true in that
model.
However, #18311 also exposes that the Resize implementation for `linear`
mode has bug. It seems that the currently implementation always treat
the input as either 2d or 4d tensor, however, the actual input is 3d
tensor, that's why the shader compilation is failed. We may need to fix
it in a separate PR.
### Description
Added Uniform support to binary ops
### 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. -->
To improve performance
### 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
* based on design document & following InferenceSession's run
implementation, implemented TrainingSession.runTrainStep
### Motivation and Context
* Adding web bindings for training
#### Related work
* #16521 allowed for training artifacts to be built
* #17333 added interfaces for training
* #17474 allowed for training package to be built + added training
backend to web package
* #17891 implementation for createTrainingSession on the TypeScript side
**[SHOULD BE MERGED IN BEFORE THIS PR]**
---------
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
Co-authored-by: Ashwini Khade <askhade@microsoft.com>
### 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
### Description
This PR enables `softmax` outputs max supported components instead of
scalar for each thread.
Softmax with input[0]: [12,4096,4096] becomes 47.86 ms from 55.11 ms
