### Description
See
454996d496
for manual changes (excluded auto-generated formatting changes)
### Why
Because the toolsets for old clang-format is out-of-date. This reduces
the development efficiency.
- The NPM package `clang-format` is already in maintenance mode. not
updated since 2 years ago.
- The VSCode extension for clang-format is not maintained for a while,
and a recent Node.js security update made it not working at all in
Windows.
No one in community seems interested in fixing those.
Choose Prettier as it is the most popular TS/JS formatter.
### How to merge
It's easy to break the build:
- Be careful of any new commits on main not included in this PR.
- Be careful that after this PR is merged, other PRs that already passed
CI can merge.
So, make sure there is no new commits before merging this one, and
invalidate js PRs that already passed CI, force them to merge to latest.
### Description
Also update the op test suite.
### Motivation and Context
Previously the *total* size in case `Expand - last dim is not divisible
by 4` was a multiple of 4, even though the *last dimension* was not, so
the bug has never been caught.
### Description
support using uniform buffer.
This PR allows to use uniform buffer in shader program, so that some
runtime information (eg. input/output shape) is no longer need to be
hardcoded into shader code.
There are 2 commits in this PR:
-
[667f31c](667f31c83d):
framework changes to support uniform buffer, as well as updates in
program manager, gpu data manager and indices helper.
-
[09e1d2a](09e1d2ad1d):
an example change for operator `Transpose` to use input's rank-only
instead of dims as shader key. With this change, model mobilenetv2-12
shader compile times dropped from 71 to 52.
### Description
This PR optimizes the gather op, which is improved ~6ms in segment
anything model in ADL.
The problem in original algorithm is that it includes a for loop to
calculate a block size of data. However, the block size may be very
large, like `65536`. In GPU shader, we should try to avoid large loop in
shader and try to use more threads to do it parallelly.
Before:
```
[profiling] kernel "41771992|[Gather] 41771992" input[0]: [4,65536] | float32, input[1]: [1] | int64, output[0]: [1,65536] | float32, execution time: 6886207 ns
```
After:
```
[profiling] kernel "41771992|[Gather] 41771992" input[0]: [4,65536] | float32, input[1]: [1] | int64, output[0]: [1,65536] | float32, execution time: 11719 ns
### Description
Since WebGPU supports only float32 and int32, having Gather, Reshape,
Shape, Squeeze and Unsqueeze ops with other data types create additional
MemCpy ops and slow down the overall execution as all other OPs with
other tensor types will be done on CPU.
Before this patch SD Unet had these numbers:
Node(s) placed on [CPUExecutionProvider]. Number of nodes: 1141
Node(s) placed on [JsExecutionProvider]. Number of nodes: 4025
memcpy tokens: 2001
After patch:
Node(s) placed on [CPUExecutionProvider]. Number of nodes: 1735
Node(s) placed on [JsExecutionProvider]. Number of nodes: 2243
memcpu tokens: 813
It also gives more than 5X performance benefit. From 12sec for one Unet
step to 2.2sec on RTX 3090 Ti, so we are almost getting to native
performance.
UPD: with latest changes from main branch and multi-threading it went
down to 1.6sec. Will try re-exporting my model to onnx with maximum
optimizations, like using MultiHeadAttention to decrease node count.
Maybe after implementing that it can go in less than 1 sec
### 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>
