### Description
Use esbuild to accelerate bundle build.
This change uses esbuild to replace webpack for onnxruntime-web. Bundle
build time reduced from ~20sec to ~0.6sec on my windows dev box.
A few changes applied:
- import nodejs modules using "node:" prefix
- remove enum declaration inside namespace (EncoderUsage)
- use "fs/promise" to replace the old promisify from "util"
- separate ort-web and test-runner. Previously they are bundled
together, now they are built into 2 files.
- optimize karma runner launch time
- remove unnecessary sourcemap preprocessor. sourcemaps are handled
inside esbuild
- remove unnecessary proxies (because ort-web and test-runner are
separated now, the path are correctly inferred)
- remove file watcher for test data
- optimize special handling as esbuild plugins:
- polyfill dummy imports for node.js modules when targetting browser.
- load as content string for ort-wasm-*.worker.js
- load as content string for ./proxy-worker/main.ts
- a source patch to ort-wasm*-threaded*.js (see details in comments in
code)
- updated debug configurations for sourcemap mapping to ensure
out-of-box good dev experience
[//]: # (## Work In Progress. Feedbacks are welcome!)
### Description
This PR adds a few properties, methods and factories to Tensor type to
support IO-binding feature. This will allow user to create tensor from
GPU/CPU bound data without a force transferring of data between CPU and
GPU.
This change is a way to resolve#15312
### Change Summary
1. Add properties to `Tensor` type:
a. `location`: indicating where the data is sitting. valid values are
`cpu`, `cpu-pinned`, `texture`, `gpu-buffer`.
b. `texture`: sit side to `data`, a readonly property of `WebGLTexture`
type. available only when `location === 'texture'`
c. `gpuBuffer`: sit side to `data`, a readonly property of `GPUBuffer`
type. available only when `location === 'gpu-buffer'`
2. Add methods to `Tensor` type (usually dealing with inference
outputs):
- async function `getData()` allows user to download data from GPU to
CPU manually.
- function `dispose()` allows user to release GPU resources manually.
3. Add factories for creating `Tensor` instances:
a. `fromTexture()` to create a WebGL texture bound tensor data
b. `fromGpuBuffer()` to create a WebGPUBuffer bound tensor data
c. `fromPinnedBuffer()` to create a tensor using a CPU pinned buffer
### Examples:
create tensors from texture and pass to inference session as inputs
```js
// when create session, specify we prefer 'image_output:0' to be stored on GPU as texture
const session = await InferenceSession.create('./my_model.onnx', {
executionProviders: [ 'webgl' ],
preferredOutputLocation: { 'image_output:0': 'texture' }
});
...
const myImageTexture = getTexture(); // user's function to get a texture
const myFeeds = { input0: Tensor.fromTexture(myImageTexture, { width: 224, height: 224 }) }; // shape [1, 224, 224, 4], RGBA format.
const results = await session.run(myFeeds);
const myOutputTexture = results['image_output:0'].texture;
```
