### Description
Introduce how to use matmul_4bits_quantizer to do weight only
quantization.
### Motivation and Context
Add 4bit quantizer to onnx runtime doc
This pull request addresses several spelling errors and inconsistencies
in the capitalization of proper nouns within the documentation.
### Motivation and Context
To improve the quality of the documentation, spelling errors and
capitalization mistakes have been corrected. This ensures that the
content is more accurate and easier to read.
DRAFT PR until images and thumbnail are provided
---------
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
Co-authored-by: Emma Ning <43255631+EmmaNingMS@users.noreply.github.com>
This documentation adds documentation on:
- how to allocate CUDA device tensors from C++ and python
- how to use DML device tensors from C++ and python
- it also shows how to leverage existing GPU allocations in ORT
- how to overlap PCI copies and GPU execution using CUDA streams
- how to overlap PCI copies and GPU execution using D3D12 Command Lists
and custom resources
---------
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
- doc suggests that QDQ model are created with dynamic quant, which is
not the case anymore.
- updating and restructuring the doc
### Description
<!-- Describe your changes. -->
- QDQ model format representation doesn't come for dynamic quantization,
but the doc was suggesting.
- May be a couple of years back this support was there but not anymore
### 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. -->
- Clears misunderstanding that QDQ Onnx models can be created with
dynamic quantization.
https://github.com/microsoft/onnxruntime/issues/20125
### Description
Adding cookie consent for google analytics (Adobe WIP), daisyui4 changes
and updates, updates to events page, and added MD blog support (for
upcoming blogs).
Staged at: https://maanavd.github.io/onnxruntime/
---------
Co-authored-by: MaanavD <maanavdalal@microsoft.com>
### Description
Added docs for ONNX 1.17 covering logging, tracing, and QNN EP Profiling
### Motivation and Context
- ONNX Logging has not been documented
- ONNX Tracing with Windows has barely been documented
- ONNX 1.17 has new tracing and QNN EP Profiling
PRs: #16259, #18201, #18882, #19397
Command was split from `shape_inference.py` to address import sequence
warnings in 8004db4bf1 and was not
updated.
Anyone following the docs prior to this change would have encountered;
```
root@Raze:~# python -m onnxruntime.quantization.shape_inference --help
/usr/lib/python3.10/runpy.py:126: RuntimeWarning: 'onnxruntime.quantization.shape_inference' found in sys.modules after import of package 'onnxruntime.quantization', but prior to execution of 'onnxruntime.quantization.shape_inference'; this may result in unpredictable behaviour
warn(RuntimeWarning(msg))
```
Instead of the expected;
```
root@Raze:~# ./onnxtesting/bin/python -m onnxruntime.quantization.preprocess --help
usage: preprocess.py [-h] --input INPUT --output OUTPUT [--skip_optimization SKIP_OPTIMIZATION] [--skip_onnx_shape SKIP_ONNX_SHAPE]
[--skip_symbolic_shape SKIP_SYMBOLIC_SHAPE] [--auto_merge] [--int_max INT_MAX] [--guess_output_rank]
[--verbose VERBOSE] [--save_as_external_data] [--all_tensors_to_one_file]
[--external_data_location EXTERNAL_DATA_LOCATION]
[--external_data_size_threshold EXTERNAL_DATA_SIZE_THRESHOLD]
Model optimizer and shape inferencer[continues]
```
### Description
-WPA OSS plugins
-Pertetto UI which is recommended by Google over deprecated
chrome://tracing experience
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- I tried both chrome://tracing and Perfetto to open the .json and it's
not a great expreince. If on Windows, WPA is a MUCH better experience
and easier to work with the data/report. Also Google recommends Perfetto
vs chrome://tracing so updated that as well
### Description
revise example C++ code of using file path.
normalize the example code to use macros `ORTCHAR_T` and `ORT_TSTR`
defined in onnxruntime_c_api.h so that the code works in both Windows
and UNIX.
This PR resolves#14859
* custom.md - use release tags
* Rename mobile performance tuning doc.
* Move old mobile performance tuning docs.
* remove title
* Adapt original documentation and add more for 1.11.
* Adjust doc ordering.
* Update version text.
* Update nav, fix links.
* rework docs
* Explain the usage of dynamic cost model
* add tool usage
* fix a few grammar
* refactor
* refactor
* refactor
* fixing comments
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
