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e5ca3f3dcb
[//]: # (## 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; ```
58 lines
1.8 KiB
TypeScript
58 lines
1.8 KiB
TypeScript
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import {CpuPinnedConstructorParameters, GpuBufferConstructorParameters, TextureConstructorParameters} from './tensor-factory.js';
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import {Tensor} from './tensor-impl.js';
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/**
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* calculate size from dims.
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*
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* @param dims the dims array. May be an illegal input.
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*/
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export const calculateSize = (dims: readonly unknown[]): number => {
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let size = 1;
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for (let i = 0; i < dims.length; i++) {
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const dim = dims[i];
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if (typeof dim !== 'number' || !Number.isSafeInteger(dim)) {
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throw new TypeError(`dims[${i}] must be an integer, got: ${dim}`);
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}
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if (dim < 0) {
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throw new RangeError(`dims[${i}] must be a non-negative integer, got: ${dim}`);
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}
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size *= dim;
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}
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return size;
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};
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/**
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* implementation of Tensor.reshape()
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*/
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export const tensorReshape = (tensor: Tensor, dims: readonly number[]): Tensor => {
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switch (tensor.location) {
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case 'cpu':
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return new Tensor(tensor.type, tensor.data, dims);
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case 'cpu-pinned':
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return new Tensor({
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location: 'cpu-pinned',
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data: tensor.data as CpuPinnedConstructorParameters['data'],
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type: tensor.type as CpuPinnedConstructorParameters['type'],
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dims,
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});
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case 'texture':
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return new Tensor({
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location: 'texture',
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texture: tensor.texture,
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type: tensor.type as TextureConstructorParameters['type'],
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dims,
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});
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case 'gpu-buffer':
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return new Tensor({
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location: 'gpu-buffer',
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gpuBuffer: tensor.gpuBuffer,
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type: tensor.type as GpuBufferConstructorParameters['type'],
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dims,
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});
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default:
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throw new Error(`tensorReshape: tensor location ${tensor.location} is not supported`);
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}
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};
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