### Description
- Support more test cases for WebNN EP in suite-test-list.jsonc
- Add DISABLE_WEBNN flag in build.ts as preparing for WebNN EP release
- Add test option: '--webnn-device-type' in test-runner-args-cli.ts to
support running WebNN 'gpu' deviceType
- Use Chrome Stable as default browser for WebNN testing to unblock the
CI limitation.
### Description
Add trilinear interpolation to Resize and changed activation_params attribute as optional for FuseConv.
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
### 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>
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
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### Motivation and Context
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---------
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
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### Motivation and Context
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### Description
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In previous implementation, there are two loops to iterate H * W
elements to calculate the `mean` and `squaredNorm` value in one thread,
meanwhile it outputs H * W elements in one thread. That results it's
very very slow when H * W is a large value. And usually, H * W does be a
large value in a model. For example, in the `candy-8` model, the shapes
of [H, W] are [224,224], [112,112], [56,56] for `InstanceNormalization`
op. And in my ADL, `[1,224,224,32]` consumes 17 ms. See below:
```
[profiling] kernel "23848328|[InstanceNormalization] 23848328" input[0]: [1,224,224,32] | float32, input[1]: [32] | float32, input[2]: [32] | float32, output[0]: [1,224,224,32] | float32, execution time: 17007914 ns
```
In this PR, it uses workgroup memory to optimize the original algorithm.
The advantage is that it can parallelly utilize the 64 (workgroupSize)
threads in one workgroup to calculate `mean` and `squaredNorm` value.
Meanwhile, it only outputs `H * W / workgroupSize` outputs for one
thread, which greatly reduces the overhead for one thread. With this
optimization, `[1,224,224,32]` becomes 3 ms and the main overhead is the
extra two `transpose`. The `createInstanceNormProgramInfo` only needs
`0.64` ms. See below:
```
[profiling] kernel "23003600|[InstanceNormalization] 23003600" input[0]: [1,224,224,32] | float32, output[0]: [1,32,224,224] | float32, execution time: 1543792 ns
program-manager.ts:115
[profiling] kernel "23003600|[InstanceNormalization] 23003600" input[0]: [1,32,224,224] | float32, input[1]: [32] | float32, input[2]: [32] | float32, output[0]: [1,32,224,224] | float32, execution time: 642652 ns
program-manager.ts:115
[profiling] kernel "23003600|[InstanceNormalization] 23003600" input[0]: [1,32,224,224] | float32, output[0]: [1,224,224,32] | float32, execution time: 991608 ns
```
This PR currently only applies the new algorithm to NCHW format. For
NHWC format, one way is to transpose the input so that it can use the
new algorithm. But the disadvantage is that 2 extra transpose are added.
@dakenf also gives another way to optimize NHWC. Details see
[here](d45a96616d/js/web/lib/wasm/jsep/webgpu/ops/instance-norm.ts).
I checked @dakenf's method. The perf is similar with transpose +
optimized NCHW. But on different GPUs, one is a little better than
another or vice versa. So I prefer this PR only does the NCHW part.
@dakenf can submit his optimization on NHWC.
### Description
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### Motivation and Context
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### Description
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### 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
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### Motivation and Context
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### 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
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### Motivation and Context
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### 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
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### Description
Fix a typo. LayerNormalization takes 2 or 3 inputs. The third input,
bias, is optional.
### Motivation and Context
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### Description
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### Motivation and Context
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### 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
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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### Description
Add missing L1Reduce and L2Reduce operator kernels.
### Motivation and Context
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### Description
Added support for ReduceL1, ReduceL2, ReduceMean, ReduceMin, ReduceMax,
ReduceSum, ReduceLogSum, ReduceLogSumExp, ReduceProd and
ReduceSquareSum.
### Motivation and Context
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---------
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 intends to enable WebNN EP in ONNX Runtime Web. It translates
the ONNX nodes by [WebNN
API](https://webmachinelearning.github.io/webnn/), which is implemented
in C++ and uses Emscripten [Embind
API](https://emscripten.org/docs/porting/connecting_cpp_and_javascript/embind.html#).
Temporarily using preferred layout **NHWC** for WebNN graph partitions
since the restriction in WebNN XNNPack backend implementation and the
ongoing
[discussion](https://github.com/webmachinelearning/webnn/issues/324) in
WebNN spec that whether WebNN should support both 'NHWC' and 'NCHW'
layouts. No WebNN native EP, only for Web.
**Motivation and Context**:
Allow ONNXRuntime Web developers to access WebNN API to benefit from
hardware acceleration.
**WebNN API Implementation Status in Chromium**:
- Tracked in Chromium issue:
[#1273291](https://bugs.chromium.org/p/chromium/issues/detail?id=1273291)
- **CPU device**: based on XNNPack backend, and had been available on
Chrome Canary M112 behind "#enable-experimental-web-platform-features"
flag for Windows and Linux platforms. Further implementation for more
ops is ongoing.
- **GPU device**: based on DML, implementation is ongoing.
**Open**:
- GitHub CI: WebNN currently is only available on Chrome Canary/Dev with
XNNPack backend for Linux and Windows. This is an open to reviewers to
help identify which GitHub CI should involved the WebNN EP and guide me
to enable it. Thanks!
