### Description
Convert output_padding attribute from 1D to 2D convtranspose
### 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. -->
https://github.com/microsoft/onnxruntime/issues/23403
BUG #23273
This PR does below optimizations:
1. When output channels is one, 1) calculate the offset before the
inchannel loop to reduce indices to offsets calculation, 2) split the
`inputChannelsPerGroup` into `inputChannelsPerGroupInt` and
`inputChannelsRemainder` parts so that we can always access 4 data for
`inputChannelsPerGroupInt`.
2. Use precise initial value to reduce useless loop iterations. Thanks
@jiangzhaoming 's suggestion's on this.
With this PR, ConvTranspose becomes 3.7s from 8.4s on Intel Meteor Lake.
On NV RTX 2000 Ada, it becomes 1.6s from 2.7s.
### Description
<!-- Describe your changes. -->
BUG #23273
With this change, I see the convTranspose time in that bug becomes ~7s
from ~90s on my Meteor Lake.
This PR does below things:
1. Use stride to update the increasement in the loop.
In the bug, the stride is 1024, which can greatly reduce the loop times.
2. Support components for A to reduce the memory access times.
3. When output channels is 1, the b components can be same with A to
further reduce the memory access times.
### Description
Avoid using vec4 Matmul implementation for ConvTranspose with channel-last
### 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 implementation using MatMul to increase perf.
### 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 JSEP ConvTranspose shader code errors.
### 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. -->
