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---------
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
Avoid producing presentKey/presentValue outputs if pastKey/pastValue
don't exists.
### Description
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### Description
Previously, MultiHeadAttention supports relative position bias of shape
[1, N, S, T] or [B, N, S, T], and DecoderMaskedMultiHeadAttention
supports [1, N, S, T]. This will extend the support to allow [1, N, S,
T], [B, N, S, T], [B, 1, S, T] and [1, 1, S, T] for CUDA and CPU EPs.
- [x] Rename the input of "relative position bias" to "attention bias"
because it can also be used for other types of bias, like ALiBi
(Attention with Linear Biases) or attention mask.
- [x] Update unfused kernel to support broadcasting 2nd dimension of
attention bias.
- [x] Update efficient attention to support broadcasting 2nd dimension
of attention bias.
- [x] Update operators (MultiHeadAttention,
DecoderMaskedMultiHeadAttention, Attention, PackedAttention,
PackedMultiHeadAttention) to support broadcast attention bias on CUDA
and CPU EPs.
- [x] Update ROCm, DML and WebGPU naming to be consistent. (Note that
those EPs do not support broadcasting attention_bias for now).
- [x] Add attention bias tests for MultiHeadAttention.
- [x] Update operator documents
- [x] Update benchmark script
Other changes:
* Fix some checks in multihead-attention.ts
* Add helper functions to dump tensors given dimensions.
Bug: https://github.com/microsoft/onnxruntime/issues/21386
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### Description
Added DequantizeLinear operator for JSEP.
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### Description
allow op test to use f16 type for inputs/outputs.
This PR introduces "@petamoriken/float16" as Float16Array polyfill but
restricts it to be only used for test runner.
Bug:https://github.com/microsoft/onnxruntime/issues/21467
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### Description
Remove explicitly concatinating pastKey with Key and pastValue with
Value.
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### Description
The Key and Value inputs could be 4-dims
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### Description
Fixed pastkey, key and pastvalue, value concatenation condition and
fixed index error. Added new test cases.
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### Description
Enabled more usecases
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### Description
fix test runner with optional input/output.
This change fixes the OP test runner (.jsonc format test) with optional
input(s) and/or output(s).
this fix reveals a problem of dealing with optional outputs:
> Take SkipSimplifiedLayerNorm as example:
>
> if in the ONNX model, the node's outputs are: [ 'output_0', '' ]
instead of [ 'output_0' ], the current implementation will fail. The
difference is, in the first case, context.outputCount == 2, and then the
typescript implementation will try to create a tensor for output[1]. It
will eventually call to C++ function (OpKernelContext::Output), and the
output.DataRaw() will be nullptr. WebGPU backend will fail because it
cannot deal with a TensorView with data == 0.
>
This problem may need to be fixed or workaround in separated PR. This PR
does not fix this problem. Failed test cases are modified to work -
please note this PR does not break those test cases as they never work.
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Improve performance using shared memory
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### Description
Avoid using vec4 Matmul implementation for ConvTranspose with channel-last
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### Description
For Concat operation, the zero-size input tensor shape need to be
preserved and, unlike non-zero tensors, the dims are not constrained to
match other input tensors' dims.
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### Description
Fixes build break brought by #19614
Currently WebGL backend does not support zero sized tensor. This change
split test data into 2 parts, and only enable zero sized tensor tests
for WebGPU.
### Description
This PR allows zero-sized output.
To make the implementation simple, it does not support partial
zero-sized tensor. Which means, either all outputs are zero-sized, or an
error will be reported.
added 2 tests:
- op test of `Add` with input T[2,0] T[2,1], and
- test_split_zero_size_splits
### Description
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1. Fix Where operator to handle Boolean input less than 4 bytes.
2. Fix JSEP test harness to use tensor names consistently.
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### Description
Add MatMulNBits to support MatMul using 4-bit quantized weights
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### Description
This PR 1) adds LeakyRelu activation for fusedConv; 2) makes `vec4<f16>`
value work with `float32` uniforms attributes.
For example:
`clamp(value, vec4<f16>(uniforms.clip_min),
vec4<f16>(uniforms.clip_max)` will throw compilation errors since
`uniforms.clip_min` and `uniforms.clip_min` are `f32` not `f16`. So we
need to change it to `clamp(value, vec4<f16>(f16(uniforms.clip_min)),
vec4<f16>(f16(uniforms.clip_max))`
And above problem was introduced when we make activation attributes as
uniforms instead of constant.
BTW, after adding LeakyRelu, `realesrgan-t256` model can pass.
### Description
```math
\tanh(x)=\frac{e^x-e^{-x}}{e^x+e^{-x}}=
\left\{
\begin{array}{cc}
-\frac{1-e^{-2\cdot(-x)}}{1+e^{-2\cdot(-x)}}, & x<0 \\
0, & x=0 \\
\frac{1-e^{-2x}}{1+e^{-2x}}, & x>0
\end{array}
\right.
```
### Motivation and Context
On some platforms,
$$\tanh(1000)=\frac{e^{1000}-e^{-1000}}{e^{1000}+e^{-1000}}$$ would
produce NaN instead of 0.999... or 1 (imagine $e^{1000}=\infty$ and
$\frac{\infty}{\infty}$ explodes).
### Description
This PR provides a vectorized algorithm for NHWC GroupedConv to improve
performance.
The aggregate time of GroupedConv in mobilenetv2-12 becomes ~1ms from
~4ms on Intel Alder Lake machine. About 20% improvement for the whole
model.
### 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.
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### Description
Add trilinear interpolation to Resize and changed activation_params attribute as optional for FuseConv.
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### Description
Add uinforms to Einsum
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Improve performance.
### 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
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.
