diff --git a/.gitignore b/.gitignore index f32fea01..3dd5fff1 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,9 @@ /tools/**/*.js /tools/**/*.js.map +/benchmark/**/node_modules/ +/benchmark/**/dist/ + npm-debug.log .DS_Store yarn-error.log diff --git a/benchmark/LICENSE b/benchmark/resnet/LICENSE similarity index 100% rename from benchmark/LICENSE rename to benchmark/resnet/LICENSE diff --git a/benchmark/README.md b/benchmark/resnet/README.md similarity index 100% rename from benchmark/README.md rename to benchmark/resnet/README.md diff --git a/benchmark/karma.conf.js b/benchmark/resnet/karma.conf.js similarity index 100% rename from benchmark/karma.conf.js rename to benchmark/resnet/karma.conf.js diff --git a/benchmark/package.json b/benchmark/resnet/package.json similarity index 100% rename from benchmark/package.json rename to benchmark/resnet/package.json diff --git a/benchmark/run-benchmark.js b/benchmark/resnet/run-benchmark.js similarity index 100% rename from benchmark/run-benchmark.js rename to benchmark/resnet/run-benchmark.js diff --git a/benchmark/src/imagenet.js b/benchmark/resnet/src/imagenet.js similarity index 100% rename from benchmark/src/imagenet.js rename to benchmark/resnet/src/imagenet.js diff --git a/benchmark/src/index.js b/benchmark/resnet/src/index.js similarity index 100% rename from benchmark/src/index.js rename to benchmark/resnet/src/index.js diff --git a/benchmark/webpack.conf.js b/benchmark/resnet/webpack.conf.js similarity index 100% rename from benchmark/webpack.conf.js rename to benchmark/resnet/webpack.conf.js diff --git a/benchmark/super_resolution_model_zoo/LICENSE b/benchmark/super_resolution_model_zoo/LICENSE new file mode 100644 index 00000000..158ec504 --- /dev/null +++ b/benchmark/super_resolution_model_zoo/LICENSE @@ -0,0 +1,5 @@ +# Licenses + +TensorFlow.js: +https://github.com/tensorflow/tfjs/blob/master/LICENSE + diff --git a/benchmark/super_resolution_model_zoo/README.md b/benchmark/super_resolution_model_zoo/README.md new file mode 100644 index 00000000..1b66be98 --- /dev/null +++ b/benchmark/super_resolution_model_zoo/README.md @@ -0,0 +1,38 @@ +# Benchmarks +This sub project is to benchmark model zoo's super resolution model and compare ONNX.js peformance vs other leading in-browser AI Inference frameworks. + +## Frameworks +- TensorFlow.js +- ONNX.js + +## Backends +- WebGL + +## Browsers +(not all framework/backend combinations are supported by all browsers) +- Chrome (WebGL 2) +- Edge (WebGL 1) + +## Instructions + +1. Ensure that the ONNX.js project (the parent) is already installed and built: +```bash +npm ci +npm run build +``` +2. Change to `benchmark/super_resolution_model_zoo` subfolder and run npm ci and build in the benchmark folder +```bash +cd benchmark +npm install +npm run build +``` +3. Run tests (Chrome) +```bash +npm run test +``` + +The test command supports enabling pack mode through environment variables, use: +```bash +PACK=1 npm run test +``` +to enable webGL texture packing for onnxjs and tfjs. diff --git a/benchmark/super_resolution_model_zoo/data/model-onnx/super_resolution.onnx b/benchmark/super_resolution_model_zoo/data/model-onnx/super_resolution.onnx new file mode 100644 index 00000000..53283c05 Binary files /dev/null and b/benchmark/super_resolution_model_zoo/data/model-onnx/super_resolution.onnx differ diff --git a/benchmark/super_resolution_model_zoo/karma.conf.js b/benchmark/super_resolution_model_zoo/karma.conf.js new file mode 100644 index 00000000..1daabad6 --- /dev/null +++ b/benchmark/super_resolution_model_zoo/karma.conf.js @@ -0,0 +1,68 @@ +// Karma configuration +const path = require('path') +function getMachineIpAddress() { + var os = require('os'); + var ifaces = os.networkInterfaces(); + + for (const ifname in ifaces) { + for (const iface of ifaces[ifname]) { + if ('IPv4' !== iface.family || iface.internal !== false) { + // skip over internal (i.e. 127.0.0.1) and non-ipv4 addresses + continue; + } + + // returns the first available IP address + return iface.address; + } + } + + // if no available IP address, fallback to "localhost". + return 'localhost'; +} + +module.exports = function(config) { + config.set({ + basePath: './', + frameworks: ['mocha'], + files: [ + { pattern: 'dist/main.js' }, + { pattern: 'dist/onnx-wasm.wasm', included: false}, + { pattern: 'dist/onnx-worker.js', included: false}, + { pattern: 'data/**/*', watched: false, included: false, served: true, nocache: true } + ], + proxies: { + '/onnx-wasm.wasm': '/base/dist/onnx-wasm.wasm', + '/onnx-worker.js': '/base/dist/onnx-worker.js', + }, + exclude: [ + ], + // available preprocessors: https://npmjs.org/browse/keyword/karma-preprocessor + preprocessors: { + }, + reporters: ['mocha'], + captureTimeout: 120000, + reportSlowerThan: 100, + browserDisconnectTimeout: 600000, + browserNoActivityTimeout: 300000, + browserDisconnectTolerance: 0, + browserSocketTimeout: 60000, + logLevel: config.LOG_VERBOSE, + hostname: getMachineIpAddress(), + customLaunchers: { + ChromeTest: {base: 'Chrome', flags: ['--window-size=1,1']}, + ChromeDebug: {debug: true, base: 'Chrome', flags: ['--remote-debugging-port=9333']} + }, + client: { + captureConsole: true, + mocha: {expose: ['body'], timeout: 3000000}, + browser: config.browsers, + printMatches: false, + // To enable pack, run 'PACK=1 npm run test' + usePackedGlTexture: config.usePackedGlTexture==1 ? true : false, + runIteration: config.runIteration ? config.runIteration : 10, + profile: config.profile + }, + browsers: ['ChromeTest', 'ChromeDebug', 'Edge', 'Safari'], + browserConsoleLogOptions: {level: "debug", format: "%b %T: %m", terminal: true}, + }) +} diff --git a/benchmark/super_resolution_model_zoo/package-lock.json b/benchmark/super_resolution_model_zoo/package-lock.json new file mode 100644 index 00000000..1414d84f --- /dev/null +++ b/benchmark/super_resolution_model_zoo/package-lock.json @@ -0,0 +1,12349 @@ +{ + "name": "super-resolution-benchmarks", + "version": "1.0.0", + "lockfileVersion": 2, + "requires": true, + "packages": { + "": { + "name": "super-resolution-benchmarks", + "version": "1.0.0", + "license": "ISC", + "dependencies": { + "@tensorflow/tfjs": "^3.4.0", + "lodash": "^4.17.11", + "ndarray": "^1.0.18", + "ndarray-ops": "^1.2.2", + "onnxjs": "file:../../" + }, + "devDependencies": { + "blueimp-load-image": "^2.19.0", + "karma": "^6.3.2", + "karma-chrome-launcher": "^2.2.0", + "karma-edge-launcher": "^0.4.2", + "karma-mocha": "^2.0.1", + "karma-mocha-reporter": "^2.2.5", + "karma-requirejs": "^1.1.0", + "karma-safari-launcher": "^1.0.0", + "mocha": "^8.3.2", + "requirejs": "^2.3.6", + "webpack": "^4.24.0", + "webpack-cli": "^3.1.2" + } + }, + "../..": { + "version": "0.1.8", + "license": "MIT", + "dependencies": { + "flatbuffers": 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--runIteration=$RUNCOUNT", + "profile": "karma start --browsers ChromeTest --single-run --profile --usePackedGlTexture=$PACK --runIteration=$RUNCOUNT", + "test-debug": "karma start --browsers ChromeDebug", + "test-edge": "karma start --browsers Edge --single-run", + "test-safari": "karma start --browsers Safari --single-run" + }, + "keywords": [ + "super-resolution", + "benchmark" + ], + "author": "", + "license": "ISC", + "devDependencies": { + "blueimp-load-image": "^2.19.0", + "karma": "^6.3.2", + "karma-chrome-launcher": "^2.2.0", + "karma-edge-launcher": "^0.4.2", + "karma-mocha": "^2.0.1", + "karma-mocha-reporter": "^2.2.5", + "karma-requirejs": "^1.1.0", + "karma-safari-launcher": "^1.0.0", + "mocha": "^8.3.2", + "requirejs": "^2.3.6", + "webpack": "^4.24.0", + "webpack-cli": "^3.1.2" + }, + "dependencies": { + "@tensorflow/tfjs": "^3.4.0", + "lodash": "^4.17.11", + "ndarray": "^1.0.18", + "ndarray-ops": "^1.2.2", + "onnxjs": "file:../../" + } +} diff --git a/benchmark/super_resolution_model_zoo/src/index.js b/benchmark/super_resolution_model_zoo/src/index.js new file mode 100644 index 00000000..725ed246 --- /dev/null +++ b/benchmark/super_resolution_model_zoo/src/index.js @@ -0,0 +1,290 @@ +import * as tf from '@tensorflow/tfjs'; +import loadImage from 'blueimp-load-image'; +import ndarray from 'ndarray'; +import ops from 'ndarray-ops'; +import * as onnx from 'onnxjs'; + +// TODO this benchmark is extended based on the old resnet benchmark. Merge these 2 into a generic benchmarking tool. + +const IMAGE_URLS = [ + { name: 'cat', url: 'https://i.imgur.com/CzXTtJV.jpg' }, + ]; +const SERVER_BASE_PATH = '/base'; + +const BackendMapping = { + 'ONNX.js' : { + 'webgl': 'GPU-webgl', + }, + 'TensorFlow.js': { + 'webgl': 'GPU-webgl', + }, +} + +const BenchmarkImageNetData = [ + { + model: 'superResolution', + imageSize: 224, + testCases: [ + { + impl: 'TensorFlow.js', + modelPath: ``, + backends: [ 'webgl'], + inputs: IMAGE_URLS, + webglLevels: [2] + }, + { + impl: 'ONNX.js', + modelPath: `${SERVER_BASE_PATH}/data/model-onnx/super_resolution.onnx`, + backends: [ 'webgl'], + inputs: IMAGE_URLS, + webglLevels: [2] + }, + ] + }, +]; +class ImageLoader { + constructor(imageWidth, imageHeight) { + this.canvas = document.createElement('canvas'); + this.canvas.width = imageWidth; + this.canvas.height = imageHeight; + this.ctx = this.canvas.getContext('2d'); + } + async getImageData(url) { + await this.loadImageAsync(url); + const imageData = this.ctx.getImageData(0, 0, this.canvas.width, this.canvas.height); + return imageData; + } + loadImageAsync(url) { + return new Promise((resolve, reject)=>{ + this.loadImageCb(url, ()=>{ + resolve(); + }); + }); + } + loadImageCb(url, cb) { + loadImage( + url, + img => { + if (img.type === 'error') { + throw `Could not load image: ${url}`; + } else { + // load image data onto input canvas + this.ctx.drawImage(img, 0, 0) + //console.log(`image was loaded`); + window.setTimeout(() => { cb(); }, 0); + } + }, + { + maxWidth: this.canvas.width, + maxHeight: this.canvas.height, + cover: true, + crop: true, + canvas: true, + crossOrigin: 'Anonymous' + } + ); + } +} +function createBenchmark(name) { + switch (name) { + case 'TensorFlow.js': return new TensorFlowResnetBenchmark(); + case 'ONNX.js': return new OnnxJsResnetBenchmark(); + } +} +async function runBenchmark(benchmarkData, backend, imageSize) { + console.log(`runBenchmark is being called with ${benchmarkData.impl}, ${backend}, ${imageSize}`) + const impl = createBenchmark(benchmarkData.impl); + console.log(`impl: ${benchmarkData.impl}, modelPath: ${benchmarkData.modelPath}`) + await impl.init(backend, benchmarkData.modelPath, imageSize); + const imageLoader = new ImageLoader(imageSize, imageSize); + const durations = []; + for(const input of benchmarkData.inputs) { + console.log(`Running ${input.name} for ${runIteration} iterations.`) + const imageData = await imageLoader.getImageData(input.url); + for(let i = 0 ; i < runIteration; i++) { + const outputData = await impl.runModel(imageData.data); + durations.push(impl.duration); + } + } + if(profile) { + impl.endProfiling(); + } + durations.shift(); + const sum = durations.reduce((a,b)=>a+b); + const avg = sum / durations.length; + console.log(`avg duration: ${avg}`); + return { + framework: benchmarkData.impl, + backend: BackendMapping[benchmarkData.impl][backend], + duration: avg + }; +} + +class TensorFlowResnetBenchmark { + async init(backend, modelPath, imageSize) { + this.imageSize = imageSize; + tf.disposeVariables(); + tf.env().set('WEBGL_PACK', pack_texture); + + console.log(`Pack mode enabled: ${tf.env().getBool('WEBGL_PACK')}`); + if(backend) { + console.log(`Setting the backend to ${backend}`); + tf.setBackend(backend); + } + const model = tf.sequential(); + model.add(tf.layers.conv2d({ + inputShape: [224,224,1], + kernelSize: [5,5], + padding: 'same', + filters: 64, + strides: 1, + activation: 'relu', + useBias: true, + biasInitializer: 'ones', + kernelInitializer: 'varianceScaling' + })); + model.add(tf.layers.conv2d({ + kernelSize: 3, + padding: 'same', + filters: 64, + strides: 1, + activation: 'relu', + useBias: true, + biasInitializer: 'ones', + kernelInitializer: 'varianceScaling' + })); + model.add(tf.layers.conv2d({ + kernelSize: 3, + padding: 'same', + filters: 32, + strides: 1, + activation: 'relu', + useBias: true, + biasInitializer: 'ones', + kernelInitializer: 'varianceScaling' + })); + model.add(tf.layers.conv2d({ + kernelSize: 3, + padding: 'same', + filters: 9, + strides: 1, + activation: 'relu', + useBias: true, + biasInitializer: 'ones', + kernelInitializer: 'varianceScaling' + })); + model.add(tf.layers.reshape({targetShape: [1,3,3,224,224]})) + model.add(tf.layers.permute({dims: [1, 4, 2, 5, 3]})); + model.add(tf.layers.reshape({targetShape: [1,672,672]})); + + this.model = model; + } + async runModel(data) { + const inputTensor = this.preprocess(data, this.imageSize, this.imageSize); + const start = performance.now(); + const output = await this.model.predict(inputTensor); + const outputData = output.dataSync(); + const stop = performance.now(); + this.duration = stop - start; + console.log(`Duration:${this.duration}ms`); + return outputData; + } + preprocess(data, width, height) { + // data processing + const dataTensor = ndarray(new Float32Array(data), [width, height, 1]) + const dataProcessedTensor = ndarray(new Float32Array(width * height * 1), [1, width, height, 1]) + + ops.subseq(dataTensor.pick(null, null, 2), 103.939) + ops.subseq(dataTensor.pick(null, null, 1), 116.779) + ops.subseq(dataTensor.pick(null, null, 0), 123.68) + ops.assign(dataProcessedTensor.pick(0, null, null, 0), dataTensor.pick(null, null, 2)) + ops.assign(dataProcessedTensor.pick(0, null, null, 1), dataTensor.pick(null, null, 1)) + ops.assign(dataProcessedTensor.pick(0, null, null, 2), dataTensor.pick(null, null, 0)) + + return tf.tensor(dataProcessedTensor.data, dataProcessedTensor.shape); + } + endProfiling() { + } +} +class OnnxJsResnetBenchmark { + async init(backend, modelPath, imageSize) { + onnx.backend.webgl.pack = pack_texture; + console.log(`Pack mode enabled: ${onnx.backend.webgl.pack}`); + + this.imageSize = imageSize; + const hint = {backendHint: backend }; + const profilerConfig = profile ? {maxNumberEvents: 65536} : undefined; + const loggerConfig = profile ? {logLevel: 'verbose'} : undefined; + const sessionConfig = {hint, profiler: profilerConfig, logger: loggerConfig}; + + this.model = new onnx.InferenceSession(sessionConfig); + if (profile) { + this.model.startProfiling(); + } + + await this.model.loadModel(modelPath); + } + async runModel(data) { + const preprocessedData = this.preprocess(data, this.imageSize, this.imageSize); + const start = performance.now(); + + const outputMap = await this.model.run([preprocessedData]); + const outputData = outputMap.values().next().value.data; + const stop = performance.now(); + this.duration = stop - start; + console.log(`Duration:${this.duration}ms`); + return outputData; + } + delay(ms) + { + return new Promise(resolve => setTimeout(resolve, ms)); + } + preprocess(data, width, height) { + // data processing + const dataTensor = ndarray(new Float32Array(data), [width, height, 1]); + const dataProcessedTensor = ndarray(new Float32Array(width * height * 1), [1, 1, width, height]); + + ops.divseq(dataTensor, 128.0); + ops.subseq(dataTensor, 1.0); + + ops.assign(dataProcessedTensor.pick(0, 0, null, null), dataTensor.pick(null, null, 2)); + ops.assign(dataProcessedTensor.pick(0, 1, null, null), dataTensor.pick(null, null, 1)); + ops.assign(dataProcessedTensor.pick(0, 2, null, null), dataTensor.pick(null, null, 0)); + + const tensor = new onnx.Tensor(dataProcessedTensor.data, 'float32', [1, 1, width, height]); + return tensor; + } + endProfiling() { + this.model.endProfiling(); + } +} +const results = []; +const browser = __karma__.config.browser[0]; +const profile = __karma__.config.profile; +const pack_texture = __karma__.config.usePackedGlTexture; +const runIteration = __karma__.config.runIteration; + +console.log(`browser: ${browser}`) +describe('super resolution Tests', ()=> { + for(const modelTestcase of BenchmarkImageNetData) { + describe(`model: ${modelTestcase.model}`, ()=> { + for(const testCase of modelTestcase.testCases) { + for(const backend of testCase.backends) { + it(`testCase:${testCase.impl} ${backend}`, + async function() { + // rule 1: if only supports WebGL 2 then skip Edge + if(browser.startsWith('Edge') && backend === 'webgl' && !testCase.webglLevels.includes(1)) { + this.skip(); + return; + } + results.push(await runBenchmark(testCase, backend, modelTestcase.imageSize)); + } + ); + } + } + }); + } + after('printing results', ()=> { + console.log(JSON.stringify(results)); + }); +}); diff --git a/benchmark/super_resolution_model_zoo/webpack.conf.js b/benchmark/super_resolution_model_zoo/webpack.conf.js new file mode 100644 index 00000000..e8c8461e --- /dev/null +++ b/benchmark/super_resolution_model_zoo/webpack.conf.js @@ -0,0 +1,33 @@ +const webpack = require("webpack"); +const path = require('path'); +const fs = require('fs'); +const APP_DIR = path.resolve(__dirname, "./src/"); +const DIST_DIR = path.resolve(__dirname, "./dist/"); + +if (!fs.existsSync('dist')){ + fs.mkdirSync('dist'); +} +fs.createReadStream('../../dist/onnx-wasm.wasm').pipe(fs.createWriteStream('dist/onnx-wasm.wasm')); +fs.createReadStream('../../dist/onnx-worker.js').pipe(fs.createWriteStream('dist/onnx-worker.js')); + +module.exports = (env, argv) => { + const config = { + entry: APP_DIR + "/index.js", + output : { + path : DIST_DIR, + filename: "main.js" + }, + node: {fs: 'empty'}, + resolve: { + extensions: ['.js'] + } + }; + if (argv.mode === 'production') { + config.mode = 'production'; + config.devtool = 'source-map'; + } else { + config.mode = 'development'; + config.devtool = 'eval-source-map'; + } + return config; +}; diff --git a/lib/api/inference-session-impl.ts b/lib/api/inference-session-impl.ts index 7931e3fa..f1160443 100644 --- a/lib/api/inference-session-impl.ts +++ b/lib/api/inference-session-impl.ts @@ -1,6 +1,7 @@ // Copyright (c) Microsoft Corporation. All rights reserved. // Licensed under the MIT license. +import {Logger} from '../instrument'; import {Session} from '../session'; import {Tensor as InternalTensor} from '../tensor'; @@ -16,7 +17,15 @@ type OutputType = InferenceSessionInterface.OutputType; export class InferenceSession implements InferenceSessionInterface { session: Session; constructor(config?: InferenceSessionInterface.Config) { - this.session = new Session(config); + const sessionConfig: Session.Config = {}; + sessionConfig.backendHint = config?.backendHint; + sessionConfig.profiler = config?.profiler; + if (config?.logger) { + const loggerConf = config?.logger; + sessionConfig.logger = {}; + sessionConfig.logger.minimalSeverity = loggerConf.logLevel ? loggerConf.logLevel as Logger.Severity : 'info'; + } + this.session = new Session(sessionConfig); } loadModel(uri: string): Promise; loadModel(blob: Blob): Promise; diff --git a/lib/api/inference-session.ts b/lib/api/inference-session.ts index 70769de1..7ad93e04 100644 --- a/lib/api/inference-session.ts +++ b/lib/api/inference-session.ts @@ -78,6 +78,16 @@ export declare namespace InferenceSession { */ flushIntervalInMilliseconds?: number; } + + /** + * represent the configuration of the logger that is used in an inference session + */ + export interface Logger { + /** + * the log level + */ + logLevel?: string; + } } /** @@ -94,6 +104,11 @@ export declare namespace InferenceSession { * specify the configuration of the profiler that used in an inference session */ profiler?: Config.Profiler; + + /** + * specify the configuration of the logger that used in an inference session + */ + logger?: Config.Logger; } /** diff --git a/lib/backends/webgl/glsl-coordinate-lib.ts b/lib/backends/webgl/glsl-coordinate-lib.ts index f971e0bf..6ad3d018 100644 --- a/lib/backends/webgl/glsl-coordinate-lib.ts +++ b/lib/backends/webgl/glsl-coordinate-lib.ts @@ -154,6 +154,10 @@ export class CoordsGlslLib extends GlslLib { result[funcName] = this.getOutputUnpacked5DCoords( outShape as [number, number, number, number, number], outTexShape as [number, number]); break; + case 6: + result[funcName] = this.getOutputUnpacked6DCoords( + outShape as [number, number, number, number, number, number], outTexShape as [number, number]); + break; default: throw new Error(`Unsupported output dimensionality: ${outShape.length}`); } @@ -473,9 +477,9 @@ export class CoordsGlslLib extends GlslLib { source = ` ivec5 getOutputCoords() { - ivec2 resTexRC = ivec2(TexCoords.yx * + ivec2 resTexRC = ivec2(TexCoords.xy * vec2(${texShape[0]}, ${texShape[1]})); - int index = resTexRC.y * ${texShape[1]} + resTexRC.x; + int index = resTexRC.y * ${texShape[0]} + resTexRC.x; ${coordsFromIndexSnippet} return ivec5(r, c, d, d2, d3); } @@ -483,6 +487,49 @@ export class CoordsGlslLib extends GlslLib { return new GlslLibRoutine(source); } + /** + * Unpacked 6D output coordinates. + */ + protected getOutputUnpacked6DCoords(shape: [number, number, number, number, number, number], texShape: [ + number, number + ]): GlslLibRoutine { + let source = ''; + const rank = shape.length; + + let strides = null; + if (rank < 2) { + strides = []; + } + + strides = new Array(rank - 1); + strides[rank - 2] = shape[rank - 1]; + for (let i = rank - 3; i >= 0; --i) { + strides[i] = strides[i + 1] * shape[i + 1]; + } + const coordsToCompute = ['r', 'c', 'd', 'd2', 'd3', 'd4']; + const coordsFromIndexSnippet = + strides + .map((stride, i) => { + const line1 = `int ${coordsToCompute[i]} = index / ${stride}`; + const line2 = i === strides.length - 1 ? + `int ${coordsToCompute[i + 1]} = index - ${coordsToCompute[i]} * ${stride}` : + `index -= ${coordsToCompute[i]} * ${stride}`; + return `${line1}; ${line2};`; + }) + .join(''); + + source = ` + ivec6 getOutputCoords() { + ivec2 resTexRC = ivec2(TexCoords.xy * + vec2(${texShape[0]}, ${texShape[1]})); + int index = resTexRC.y * ${texShape[0]} + resTexRC.x; + ${coordsFromIndexSnippet} + return ivec6(r, c, d, d2, d3, d4); + } + `; + return new GlslLibRoutine(source); + } + /** * Generates code for common UV coords computation utility functions. */ @@ -730,6 +777,8 @@ export class CoordsGlslLib extends GlslLib { return this.getUnpackedSampler4D(funcName, name, inputLayout); case 5: return this.getUnpackedSampler5D(funcName, name, inputLayout); + case 6: + return this.getUnpackedSampler6D(funcName, name, inputLayout); default: // TODO support more dimensionalities throw new Error(`Unsupported dimension ${shape.length}-D`); @@ -1092,8 +1141,7 @@ export class CoordsGlslLib extends GlslLib { return sampleTexture(${name}, uv); } `; - return new GlslLibRoutine( - source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture', 'coordinates.coordsToOffset']); + return new GlslLibRoutine(source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture']); } /** @@ -1120,7 +1168,7 @@ export class CoordsGlslLib extends GlslLib { return ${funcName}(${getSqueezedParams(params, keptDims)}); } `; - return new GlslLibRoutine(source, ['coordinates.sampleTexture']); + return new GlslLibRoutine(source, ['coordinates.sampleTexture', 'coordinates.uvFromFlat']); } const texNumR = inputLayout.width; @@ -1137,6 +1185,52 @@ export class CoordsGlslLib extends GlslLib { source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture', 'coordinates.coordsToOffset']); } + /** + * Unpacked 6D snippet. + */ + protected getUnpackedSampler6D(funcName: string, name: string, inputLayout: TextureLayout): GlslLibRoutine { + const shape = inputLayout.unpackedShape; + const stride4 = shape[5]; + const stride3 = shape[4] * stride4; + const stride2 = shape[3] * stride3; + const stride1 = shape[2] * stride2; + const stride0 = shape[1] * stride1; + + const {newShape, keptDims} = squeezeShape(shape as number[]); + if (newShape.length < shape.length) { + const newInputShape = squeezeInputShape(shape, newShape); + const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4']; + // Deep copy of input texture layout. + const newInputLayout: TextureLayout = JSON.parse(JSON.stringify(inputLayout)); + newInputLayout.unpackedShape = newInputShape; + + const source = ` + ${this.getUnpackedSamplerFromInput(funcName, name, newInputLayout).routineBody} + float ${funcName}(int row, int col, int depth, + int depth2, int depth3, int depth4) { + return ${funcName}(${getSqueezedParams(params, keptDims)}); + } + `; + return new GlslLibRoutine(source, ['coordinates.sampleTexture', 'coordinates.uvFromFlat']); + } + + const texNumR = inputLayout.width; + const texNumC = inputLayout.height; + const source = ` + float ${funcName}(int row, int col, int depth, + int depth2, int depth3, int depth4) { + + int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} + + depth2 * ${stride3} + depth3 * ${stride4} + depth4; + vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index); + return sampleTexture(${name}, uv); + + } + `; + return new GlslLibRoutine( + source, ['coordinates.uvFromFlat', 'coordinates.sampleTexture', 'coordinates.coordsToOffset']); + } + /** * This is the main function to map from the given texture coordiantes (s,t) * to logical indices for the output @@ -1181,7 +1275,7 @@ export class CoordsGlslLib extends GlslLib { const result: {[name: string]: GlslLibRoutine} = {}; this.context.programInfo.samplers.forEach((name, i) => { const layout = programInfo.inputLayouts[i]; - const shape = layout.shape; + const shape = layout.unpackedShape.length > 0 ? layout.unpackedShape : layout.shape; const rank = shape.length; let funcName = `_${name}`; result[funcName] = new GlslLibRoutine( diff --git a/lib/backends/webgl/ops/conv-pack.ts b/lib/backends/webgl/ops/conv-pack.ts index 414bf64d..cb130279 100644 --- a/lib/backends/webgl/ops/conv-pack.ts +++ b/lib/backends/webgl/ops/conv-pack.ts @@ -15,7 +15,7 @@ import {WebGLReshapePacked} from './reshape-packed'; export class WebGLConvPacked extends Conv { protected artifacts: Artifact[]; protected programInfo: ProgramInfo[]; - + protected outputShape: number[]; run(inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] { const programManager = inferenceHandler.session.programManager; const xshape = inputs[0].dims.slice(); @@ -33,8 +33,8 @@ export class WebGLConvPacked extends Conv { `autpPad:${this.autoPad}, dilations:${this.dilations}, group:${this.group}, kernelShape:${ this.kernelShape}, pads:${this.pads}, strides:${this.strides}`); - const outputShape = WebGLConv.calcOutputShape(xshape, kshape, this.dilations, this.pads, this.strides); - const im2col = new WebGLIm2ColPacked(outputShape, kshape, this.dilations, this.pads, this.strides); + this.outputShape = WebGLConv.calcOutputShape(xshape, kshape, this.dilations, this.pads, this.strides); + const im2col = new WebGLIm2ColPacked(this.outputShape, kshape, this.dilations, this.pads, this.strides); const matmul = new WebGLMatMulPacked(); const reshape = new WebGLReshapePacked(); // shape for kernel reshape @@ -76,11 +76,10 @@ export class WebGLConvPacked extends Conv { inferenceHandler.checkAndUpdateTextureForm(this.artifacts[2], runDataMatmul); programManager.run(this.artifacts[2], runDataMatmul); const matmulOutput = runDataMatmul.outputTextureData.tensor; - // reshape output const outputShapeTensor = new Tensor( - [outputShape.length], 'int32', undefined, undefined, - new Int32Array([outputShape[0], outputShape[1], outputShape[2], outputShape[3]])); + [this.outputShape.length], 'int32', undefined, undefined, + new Int32Array([this.outputShape[0], this.outputShape[1], this.outputShape[2], this.outputShape[3]])); assert(this.artifacts.length > 2, () => 'expect at least 3 artifacts created'); if (this.artifacts.length === 3) { diff --git a/lib/backends/webgl/ops/depth-to-space.ts b/lib/backends/webgl/ops/depth-to-space.ts new file mode 100644 index 00000000..93b53690 --- /dev/null +++ b/lib/backends/webgl/ops/depth-to-space.ts @@ -0,0 +1,81 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +import {Attribute} from '../../../attribute'; +import {DepthToSpace} from '../../../ops/depth-to-space'; +import {Tensor} from '../../../tensor'; +import {WebGLInferenceHandler} from '../inference-handler'; +import {Artifact, ProgramInfo, RunData} from '../types'; + +import {reshape} from './reshape'; +import {WebGLTranspose} from './transpose'; + +export class WebGLDepthToSpace extends DepthToSpace { + protected transposeProgramInfo: ProgramInfo; + + protected transposeArtifact: Artifact; + + run(inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] { + const programManager = inferenceHandler.session.programManager; + const transposePerm = this.mode === 'DCR' ? [0, 3, 4, 1, 5, 2] : [0, 1, 4, 2, 5, 3]; + const firstReshapeShape = this.mode === 'DCR' ? + [ + inputs[0].dims[0], this.blocksize, this.blocksize, inputs[0].dims[1] / this.blocksizeSqr, inputs[0].dims[2], + inputs[0].dims[3] + ] : + [ + inputs[0].dims[0], inputs[0].dims[1] / this.blocksizeSqr, this.blocksize, this.blocksize, inputs[0].dims[2], + inputs[0].dims[3] + ]; + + const transpose = new WebGLTranspose(); + const attributes = new Attribute(undefined); + attributes.set('perm', 'ints', transposePerm); + transpose.initialize(attributes); + + // First reshape + + const firstReshapedTensor = reshape(inferenceHandler, inputs[0], firstReshapeShape); + + // transpose + if (!this.transposeProgramInfo) { + this.transposeProgramInfo = transpose.createProgramInfo(inferenceHandler, [firstReshapedTensor]); + this.transposeArtifact = programManager.build(this.transposeProgramInfo); + } + const runDataTranspose = + transpose.createRunData(inferenceHandler, this.transposeProgramInfo, [firstReshapedTensor]); + inferenceHandler.checkAndUpdateTextureForm(this.transposeArtifact, runDataTranspose); + programManager.run(this.transposeArtifact, runDataTranspose); + const transposeOutput = runDataTranspose.outputTextureData.tensor; + + // Second reshape + const result = reshape(inferenceHandler, transposeOutput, [ + inputs[0].dims[0], inputs[0].dims[1] / this.blocksizeSqr, inputs[0].dims[2] * this.blocksize, + inputs[0].dims[3] * this.blocksize + ]); + return [result]; + } + + protected getOutShape(input: Tensor): number[] { + const batchSize = input.dims[0]; + const inputDepth = input.dims[1]; + const inputHeight = input.dims[2]; + const inputWidth = input.dims[3]; + if (inputDepth % (this.blocksizeSqr) !== 0) { + throw new Error('Input depth must be divisible by squared blocksize.'); + } + const outputDepth = inputDepth / this.blocksizeSqr; + const outputHeight = inputHeight * this.blocksize; + const outputWidth = inputWidth * this.blocksize; + return [batchSize, outputDepth, outputHeight, outputWidth]; + } + + createRunData(handler: WebGLInferenceHandler, programInfo: ProgramInfo, inputs: Tensor[]): RunData { + const inputTDs = inputs.map((t, i) => handler.getOrCreateTextureData(t, programInfo.inputLayouts[i])); + return { + inputTextureDatas: inputTDs, + outputTextureData: handler.createTextureDataFromLayout(programInfo.outputLayout, inputTDs[0].tensor.type), + uniformData: {} + }; + } +} diff --git a/lib/backends/webgl/ops/matmul-pack.ts b/lib/backends/webgl/ops/matmul-pack.ts index 2f9e714e..eeefa90b 100644 --- a/lib/backends/webgl/ops/matmul-pack.ts +++ b/lib/backends/webgl/ops/matmul-pack.ts @@ -4,8 +4,10 @@ import {MatMul} from '../../../ops/matmul'; import {Tensor} from '../../../tensor'; import {BroadcastUtil} from '../../../util'; +import {getGlsl} from '../glsl-source'; import {WebGLInferenceHandler} from '../inference-handler'; import {ProgramInfo, RunData, WebGLOperator} from '../types'; +import {getCoordsDataType} from '../utils'; export class WebGLMatMulPacked extends MatMul implements WebGLOperator { run(inferenceHandler: WebGLInferenceHandler, inputs: Tensor[]): Tensor[] { @@ -13,7 +15,7 @@ export class WebGLMatMulPacked extends MatMul implements WebGLOperator { } createProgramInfo(handler: WebGLInferenceHandler, inputs: Tensor[]): ProgramInfo { const hasBias = inputs.length > 2; - const processBias = hasBias ? `value += vec4(getBias(a[0]*2).xx, getBias(a[0]*2).yy);` : ``; + const processBias = hasBias ? 'result += getBiasAtOutCoords();' : ''; const aShape = inputs[0].dims; const bShape = inputs[1].dims; const outputShape = BroadcastUtil.calcShape(aShape, bShape, true); @@ -21,34 +23,43 @@ export class WebGLMatMulPacked extends MatMul implements WebGLOperator { if (!outputShape) { throw new Error('Can\'t use matmul on the given tensors'); } - const rank = outputShape.length; + + const sharedDim = aShape[aShape.length - 1]; + const sharedDimIndex = Math.ceil(sharedDim / 2); + const aRank = aShape.length; const bRank = bShape.length; - const sharedDim = aShape[aShape.length - 1]; - // TODO:fix broadcasting + + const glsl = getGlsl(handler.session.backend.glContext.version); + const coordsDataType = getCoordsDataType(outputShape.length); + const allGlChannels = ['x', 'y', 'z', 'w', 'u', 'v']; + const shaderSource = ` - vec4 process(int indices[${rank}]) { - int a[${aRank}]; - int b[${bRank}]; - bcastMatmulIndices_A(indices, a); - bcastMatmulIndices_B(indices, b); + void main() { + ${coordsDataType} rc = getOutputCoords(); + + vec4 result = vec4(0); + + for (int i = 0; i < ${sharedDimIndex}; i++) { + vec4 a = getA(${getA(allGlChannels, aRank)}); + vec4 b = getB(${getB(allGlChannels, bRank)}); + + result += (a.rrbb * b.rgrg); + result += (a.ggaa * b.baba); + } + + ${processBias} + + ${glsl.output} = result; + }`; - vec4 value; - for (int k=0; k<((${sharedDim}+1)/2); ++k) { - a[${aRank - 1}] = k; - b[${bRank - 2}] = k; - value += ${getA(aRank)}.rrbb * ${getB(bRank)}.rgrg; - value += ${getA(aRank)}.ggaa * ${getB(bRank)}.baba; - } - ${processBias} - return value; - }`; return { inputLayouts: inputs.map((t, i) => handler.getOrCreateTextureLayout(t, 4, true, inputs[i].dims, true)), outputLayout: handler.createTextureLayoutFromShape(outputShape, 4, outputShape, {isPacked: true, reverseWH: true}), samplers: hasBias ? ['A', 'B', 'Bias'] : ['A', 'B'], shaderSource, + hasMain: true, expectPackedInputs: true, expectPackedOutputs: true, }; @@ -64,22 +75,22 @@ export class WebGLMatMulPacked extends MatMul implements WebGLOperator { } } -function getA(outputRank: number): string { - let res = 'getA('; - for (let i = 0; i < outputRank - 2; i++) { - res += `a[${i}], `; +function getA(allGlChannels: string[], rank: number): string { + let res = ''; + for (let i = 0; i < rank - 2; i++) { + res += `rc.${allGlChannels[i]}, `; } - res += `a[${outputRank - 2}]*2, ` + - 'k*2)'; + res += `rc.${allGlChannels[rank - 2]}, ` + + 'i<<1'; return res; } -function getB(outputRank: number): string { - let res = 'getB('; - for (let i = 0; i < outputRank - 2; i++) { - res += `b[${i}], `; +function getB(allGlChannels: string[], rank: number): string { + let res = ''; + for (let i = 0; i < rank - 2; i++) { + res += `rc.${allGlChannels[i]}, `; } - res += 'k*2, ' + - `b[${outputRank - 1}]*2)`; + res += 'i<<1, ' + + `rc.${allGlChannels[rank - 1]}`; return res; } diff --git a/lib/backends/webgl/program-manager.ts b/lib/backends/webgl/program-manager.ts index 36cedbf8..afcac18b 100644 --- a/lib/backends/webgl/program-manager.ts +++ b/lib/backends/webgl/program-manager.ts @@ -69,15 +69,21 @@ export class ProgramManager { return this.profiler.event('backend', 'ProgramManager.build', () => { const preprocessor = new GlslPreprocessor(this.glContext, programInfo); const fragScript = preprocessor.preprocess(); - const program = this.compile(fragScript); - const artifact = { - programInfo, - program, - uniformLocations: this.getUniformLocations( - program, preprocessor.context.programInfo.samplers, preprocessor.context.programInfo.variables), - attribLocations: this.getAttribLocations(program) - }; - return artifact; + try { + const program = this.compile(fragScript); + const artifact = { + programInfo, + program, + uniformLocations: this.getUniformLocations( + program, preprocessor.context.programInfo.samplers, preprocessor.context.programInfo.variables), + attribLocations: this.getAttribLocations(program) + }; + return artifact; + + } catch (e) { + Logger.error('ProgramManager', fragScript); + throw e; + } }); } protected doDraw(artifact: Artifact, runData: RunData): void { diff --git a/lib/ops/depth-to-space.ts b/lib/ops/depth-to-space.ts new file mode 100644 index 00000000..ac248069 --- /dev/null +++ b/lib/ops/depth-to-space.ts @@ -0,0 +1,53 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. + +import {Attribute} from '../attribute'; +import {InferenceHandler} from '../backend'; +import {Operator} from '../operators'; +import {Tensor} from '../tensor'; + +export abstract class DepthToSpace implements Operator { + constructor() {} + + abstract run(inferenceHandler: InferenceHandler, inputs: Tensor[]): Tensor[]|Promise; + + initialize(attributes: Attribute): void { + // processing node attributes + this.blocksize = attributes.getInt('blocksize'); + if (this.blocksize < 1) { + throw new Error(`blocksize must be >= 1, but got : ${this.blocksize} for DepthToSpace`); + } + this.blocksizeSqr = this.blocksize * this.blocksize; + this.mode = attributes.getString('mode', 'DCR'); + if (DepthToSpace.supportedModes.indexOf(this.mode) < 0) { + throw new Error(`unrecognized mode: ${this.mode} for DepthToSpace`); + } + } + + checkInputs(inputs: Tensor[]): boolean { + if (!inputs || inputs.length !== 1) { + return false; + } + + return this.checkInputTypes(inputs); + } + + protected checkInputTypes(inputs: Tensor[]): boolean { + const inputType = inputs[0].type; + const inputDimensionality = inputs[0].dims.length; + + // Input has to be a 4-D tensor + // TODO: Support string depth-to-space. + if (inputType === 'string' || inputDimensionality !== 4) { + return false; + } + + return true; + } + + protected mode: string; + protected blocksize: number; + protected blocksizeSqr: number; + + private static readonly supportedModes = ['DCR', 'CRD']; +} diff --git a/lib/session.ts b/lib/session.ts index e103a834..696ccc0e 100644 --- a/lib/session.ts +++ b/lib/session.ts @@ -7,7 +7,7 @@ import {promisify} from 'util'; import {Backend, SessionHandlerType} from './backend'; import {ExecutionPlan} from './execution-plan'; import {Graph} from './graph'; -import {Profiler} from './instrument'; +import {Logger, Profiler} from './instrument'; import {Model} from './model'; import {Operator} from './operators'; import {Tensor} from './tensor'; @@ -16,6 +16,7 @@ export declare namespace Session { export interface Config { backendHint?: string; profiler?: Profiler.Config; + logger?: Logger.Config; } export interface Context { @@ -30,6 +31,10 @@ export class Session { this._initialized = false; this.backendHint = config.backendHint; this.profiler = Profiler.create(config.profiler); + if (config.logger) { + // tslint:disable-next-line:no-backbone-get-set-outside-model + Logger.set('', config.logger); + } this.context = {profiler: this.profiler, graphInputTypes: [], graphInputDims: []}; } diff --git a/test/unittests/backends/webgl/test_depth_to_space.ts b/test/unittests/backends/webgl/test_depth_to_space.ts new file mode 100644 index 00000000..0569d709 --- /dev/null +++ b/test/unittests/backends/webgl/test_depth_to_space.ts @@ -0,0 +1,175 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. +import {expect} from 'chai'; + +import {Attribute} from '../../../../lib/attribute'; +import {Backend, InferenceHandler, SessionHandler} from '../../../../lib/backend'; +import {WebGLBackend} from '../../../../lib/backends/backend-webgl'; +import {WebGLInferenceHandler} from '../../../../lib/backends/webgl/inference-handler'; +import {WebGLDepthToSpace} from '../../../../lib/backends/webgl/ops/depth-to-space'; +import {Profiler} from '../../../../lib/instrument'; +import {Tensor} from '../../../../lib/tensor'; + +import {createAscendingArray} from './test_utils'; + +let backend: Backend|undefined; +let sessionhandler: SessionHandler|undefined; +let inferenceHandler: InferenceHandler|undefined; + +describe('#UnitTest# - unpacked WebGLDepthToSpace - Tensor WebGLDepthToSpace', () => { + before('Initialize Context', async () => { + const profiler = Profiler.create(); + backend = await Backend('webgl'); + sessionhandler = backend.createSessionHandler({profiler}); + inferenceHandler = sessionhandler.createInferenceHandler(); + }); + + // Set it back to false, apparently this state is sticky throughout all the tests running in same browser session.. + after('Resetting Context', () => { + (backend as WebGLBackend).pack = false; + }); + + const testDataSet = getTestData(); + for (let k = 0; k < testDataSet.length; ++k) { + const testData = testDataSet[k]; + describe(`Test concat ${JSON.stringify(testData)}`, () => {}); + it(`Test depth to space `, () => { + const webglInferenceHandler = inferenceHandler as WebGLInferenceHandler; + + // TODO support WebGl 1.0 + if (webglInferenceHandler.session.textureManager.glContext.version === 1) { + console.log('Running depth to space with webgl1 is not supported. Skipping.'); + return; + } + + const op = new WebGLDepthToSpace(); + const attributes = new Attribute(undefined); + const blocksize = testData.blocksize; + attributes.set('blocksize', 'int', blocksize); + attributes.set('mode', 'string', testData.mode as string); + + op.initialize(attributes); + const elementCount = testData.elementCount; + const inputTensorShape = testData.inputShape; + + // create input data and tensor. + const inputData = testData.rawInput ? testData.rawInput : createAscendingArray(elementCount); + const inputTensorA = new Tensor(inputTensorShape, 'float32', undefined, undefined, inputData); + + // manually creat packed texture from inputTensor, and insert in cache + webglInferenceHandler.session.textureManager.glContext.checkError(); + + webglInferenceHandler.session.textureManager.glContext.checkError(); + + const result = op.run(webglInferenceHandler, [inputTensorA]); + + webglInferenceHandler.session.textureManager.glContext.checkError(); + // verify result. + const expectedOutput = testData.expectedOutput; + expect(result[0].data).to.not.equal(null); + + expect(result[0].data).to.have.lengthOf(elementCount); + expect(result[0].data).to.deep.equal(expectedOutput); + }); + } +}); +interface TestData { + elementCount: number; + blocksize: number; + inputShape: number[]; + outputShape: number[]; + inputTextureShape: number[]; + outputTextureShape: number[]; + expectedOutput: Float32Array; + // If empty, the test will use auto-generated data. + rawInput?: Float32Array; + mode?: string; +} +function getTestData(): TestData[] { + return [ + { + elementCount: 8, + blocksize: 2, + inputShape: [1, 8, 1, 1], + outputShape: [1, 2, 2, 2], + inputTextureShape: [8, 1], + outputTextureShape: [4, 2], + rawInput: new Float32Array([0., 9., 18., 27., 36., 45., 54., 63.]), + expectedOutput: new Float32Array([0., 18., 36., 54., 9., 27., 45., 63.]), + mode: 'DCR', + }, + { + elementCount: 16, + blocksize: 2, + inputShape: [1, 8, 1, 2], + outputShape: [1, 2, 4, 2], + inputTextureShape: [1, 16], + outputTextureShape: [8, 2], + rawInput: new Float32Array([0., 1., 9., 10, 18., 19, 27., 28., 36., 37., 45., 46., 54., 55., 63., 64.]), + expectedOutput: new Float32Array([0, 18, 1, 19, 36, 54, 37, 55, 9, 27, 10, 28, 45, 63, 46, 64]), + mode: 'DCR', + }, + + { + elementCount: 48, + blocksize: 2, + inputShape: [1, 8, 2, 3], + outputShape: [1, 2, 4, 6], + inputTextureShape: [16, 3], + outputTextureShape: [8, 6], + rawInput: new Float32Array([ + 0., 1., 2., 3., 4., 5., 9., 10., 11., 12., 13., 14., 18., 19., 20., 21., + 22., 23., 27., 28., 29., 30., 31., 32., 36., 37., 38., 39., 40., 41., 45., 46., + 47., 48., 49., 50., 54., 55., 56., 57., 58., 59., 63., 64., 65., 66., 67., 68. + ]), + expectedOutput: new Float32Array([ + 0., 18., 1., 19., 2., 20., 36., 54., 37., 55., 38., 56., 3., 21., 4., 22., + 5., 23., 39., 57., 40., 58., 41., 59., 9., 27., 10., 28., 11., 29., 45., 63., + 46., 64., 47., 65., 12., 30., 13., 31., 14., 32., 48., 66., 49., 67., 50., 68. + ]), + mode: 'DCR', + }, + { + elementCount: 8, + blocksize: 2, + inputShape: [1, 8, 1, 1], + outputShape: [1, 2, 2, 2], + inputTextureShape: [8, 1], + outputTextureShape: [4, 2], + rawInput: new Float32Array([0., 9., 18., 27., 36., 45., 54., 63.]), + expectedOutput: new Float32Array([0, 9, 18, 27, 36, 45, 54, 63]), + mode: 'CRD', + }, + { + elementCount: 16, + blocksize: 2, + inputShape: [1, 8, 1, 2], + outputShape: [1, 2, 4, 2], + inputTextureShape: [1, 16], + outputTextureShape: [8, 2], + rawInput: new Float32Array([0., 1., 9., 10, 18., 19, 27., 28., 36., 37., 45., 46., 54., 55., 63., 64.]), + expectedOutput: new Float32Array([0, 9, 1, 10, 18, 27, 19, 28, 36, 45, 37, 46, 54, 63, 55, 64]), + mode: 'CRD', + }, + + { + elementCount: 48, + blocksize: 2, + inputShape: [1, 8, 2, 3], + outputShape: [1, 2, 4, 6], + inputTextureShape: [16, 3], + outputTextureShape: [8, 6], + rawInput: new Float32Array([ + 0., 1., 2., 3., 4., 5., 9., 10., 11., 12., 13., 14., 18., 19., 20., 21., + 22., 23., 27., 28., 29., 30., 31., 32., 36., 37., 38., 39., 40., 41., 45., 46., + 47., 48., 49., 50., 54., 55., 56., 57., 58., 59., 63., 64., 65., 66., 67., 68. + ]), + expectedOutput: new Float32Array([ + 0., 9., 1., 10., 2., 11., 18., 27., 19., 28., 20., 29., 3., 12., 4., 13., + 5., 14., 21., 30., 22., 31., 23., 32., 36., 45., 37., 46., 38., 47., 54., 63., + 55., 64., 56., 65., 39., 48., 40., 49., 41., 50., 57., 66., 58., 67., 59., 68. + ]), + mode: 'CRD', + }, + ]; +} diff --git a/test/unittests/backends/webgl/test_matmul_packed.ts b/test/unittests/backends/webgl/test_matmul_packed.ts new file mode 100644 index 00000000..f4eeab9b --- /dev/null +++ b/test/unittests/backends/webgl/test_matmul_packed.ts @@ -0,0 +1,247 @@ +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT license. +import {expect} from 'chai'; + +import {Backend, InferenceHandler, SessionHandler} from '../../../../lib/backend'; +import {WebGLBackend} from '../../../../lib/backends/backend-webgl'; +import {WebGLInferenceHandler} from '../../../../lib/backends/webgl/inference-handler'; +import {WebGLMatMulPacked} from '../../../../lib/backends/webgl/ops/matmul-pack'; +import {Profiler} from '../../../../lib/instrument'; +import {Tensor} from '../../../../lib/tensor'; +import {ShapeUtil} from '../../../../lib/util'; + +import {createAscendingArray} from './test_utils'; +import {createTextureFromArray} from './test_utils'; + +let backend: Backend|undefined; +let sessionhandler: SessionHandler|undefined; +let inferenceHandler: InferenceHandler|undefined; + +describe('#UnitTest# - packed matmul - Tensor matmul', () => { + before('Initialize Context', async () => { + const profiler = Profiler.create(); + backend = await Backend('webgl'); + // Explicitly set to true to trigger packed version + (backend as WebGLBackend).pack = true; + sessionhandler = backend.createSessionHandler({profiler}); + inferenceHandler = sessionhandler.createInferenceHandler(); + }); + + // Set it back to false, apparently this state is sticky throughout all the tests running in same browser session.. + after('Resetting Context', () => { + (backend as WebGLBackend).pack = false; + }); + + const testDataSet = getTestData(); + for (let k = 0; k < testDataSet.length; ++k) { + const testData = testDataSet[k]; + describe(`Test matmul ${JSON.stringify(testData)}`, () => {}); + it(`Test packed matmul kernel `, () => { + const webglInferenceHandler = inferenceHandler as WebGLInferenceHandler; + + // TODO support WebGl 1.0 + if (webglInferenceHandler.session.textureManager.glContext.version === 1) { + console.log('Running packed matmul with webgl1 is not supported. Skipping.'); + return; + } + + const op = new WebGLMatMulPacked(); + + const elementCountA = testData.elementCountA; + const elementCountB = testData.elementCountB; + + const inputTensorShapeA = testData.inputShapeA; + const inputTextureShapeA = testData.inputTextureShapeA; + + const inputTensorShapeB = testData.inputShapeB; + const inputTextureShapeB = testData.inputTextureShapeB; + + // create input data and tensor. The input data will be used to verify if the output tensor contains the + // same value but possibly different order depending on our packing algorithm. + const inputDataA = createAscendingArray(elementCountA); + const inputDataB = createAscendingArray(elementCountB); + const inputTensorA = new Tensor(inputTensorShapeA, 'float32', undefined, undefined, inputDataA); + const inputTensorB = new Tensor(inputTensorShapeB, 'float32', undefined, undefined, inputDataB); + + // manually creat packed texture from inputTensor, and insert in cache + const gl = webglInferenceHandler.session.textureManager.glContext.gl; + + webglInferenceHandler.session.textureManager.glContext.checkError(); + const webglTextureA = createTextureFromArray( + webglInferenceHandler.session.textureManager.glContext, testData.rawInputA ? testData.rawInputA : inputDataA, + gl.RGBA, inputTextureShapeA[0], inputTextureShapeA[1]); + const webglTextureB = createTextureFromArray( + webglInferenceHandler.session.textureManager.glContext, testData.rawInputB ? testData.rawInputB : inputDataB, + gl.RGBA, inputTextureShapeB[0], inputTextureShapeB[1]); + + webglInferenceHandler.session.textureManager.glContext.checkError(); + const packedShapeA = inputTextureShapeA; + const textureDataA = { + width: inputTextureShapeA[0], + height: inputTextureShapeA[1], + channels: 4 as const, + isPacked: true, + shape: packedShapeA, + strides: ShapeUtil.computeStrides(packedShapeA), + unpackedShape: inputTensorShapeA, + tensor: inputTensorA, + texture: webglTextureA! + }; + + const packedShapeB = inputTextureShapeB; + const textureDataB = { + width: inputTextureShapeB[0], + height: inputTextureShapeB[1], + channels: 4 as const, + isPacked: true, + shape: packedShapeB, + strides: ShapeUtil.computeStrides(packedShapeB), + unpackedShape: inputTensorShapeB, + tensor: inputTensorB, + texture: webglTextureB! + }; + + webglInferenceHandler.setTextureData(inputTensorA.dataId, textureDataA, true); + webglInferenceHandler.setTextureData(inputTensorB.dataId, textureDataB, true); + + const inputList = testData.biasValue ? + [ + inputTensorA, inputTensorB, + new Tensor([1], 'float32', undefined, undefined, new Float32Array([testData.biasValue])) + ] : + [inputTensorA, inputTensorB]; + + // compile shader code + const programInfo = op.createProgramInfo(inferenceHandler! as WebGLInferenceHandler, inputList); + + const artifact = webglInferenceHandler.session.programManager.build(programInfo); + webglInferenceHandler.session.programManager.setArtifact(op, artifact); + + // run kernal and get output + const runData = op.createRunData(webglInferenceHandler, artifact.programInfo, inputList); + webglInferenceHandler.session.programManager.run(artifact, runData); + const result = runData.outputTextureData.tensor.data; + + webglInferenceHandler.session.textureManager.glContext.checkError(); + // verify result. + const expectedOutput = testData.expectedOutput; + expect(result).to.not.equal(null); + let batchMultiplier = 1; + if (testData.inputShapeA.length > 2) { + batchMultiplier = testData.inputShapeA[0]; + } + if (testData.inputShapeB.length > 2) { + batchMultiplier = Math.max(batchMultiplier, testData.inputShapeB[0]); + } + + expect(result).to.have.lengthOf( + batchMultiplier * testData.inputShapeA[testData.inputShapeA.length - 2] * + testData.inputShapeB[testData.inputShapeB.length - 1]); + expect(result).to.deep.equal(expectedOutput); + }); + } +}); +interface TestData { + elementCountA: number; + elementCountB: number; + inputShapeA: number[]; + inputShapeB: number[]; + outputShape: number[]; + inputTextureShapeA: number[]; + inputTextureShapeB: number[]; + outputTextureShape: number[]; + expectedOutput: Float32Array; + // The value of bias matrix that will be broadcasted to the corresponding shape in matmul. + // i.e. If biasValue = 1, then bias matrix is [1], when being added to 2x2 matmul result, it will be bcasted to + // [1, 1] + // [1, 1] + biasValue?: number; + // If empty, the test will use auto-generated data. + rawInputA?: Float32Array; + // If empty, the test will use auto-generated data. + rawInputB?: Float32Array; +} +function getTestData(): TestData[] { + return [ + // test 2D tensor + { + elementCountA: 4, + elementCountB: 4, + inputShapeA: [2, 2], + inputShapeB: [2, 2], + outputShape: [2, 2], + inputTextureShapeA: [1, 1], + inputTextureShapeB: [1, 1], + outputTextureShape: [1, 1], + expectedOutput: new Float32Array([7, 10, 15, 22]), + }, + { + elementCountA: 4, + elementCountB: 4, + inputShapeA: [2, 2], + inputShapeB: [2, 2], + outputShape: [2, 2], + inputTextureShapeA: [1, 1], + inputTextureShapeB: [1, 1], + outputTextureShape: [1, 1], + biasValue: 1, + expectedOutput: new Float32Array([8, 11, 16, 23]), + }, + { + elementCountA: 6, + elementCountB: 6, + inputShapeA: [2, 3], + inputShapeB: [3, 2], + outputShape: [2, 2], + inputTextureShapeA: [2, 1], + inputTextureShapeB: [1, 2], + outputTextureShape: [1, 1], + expectedOutput: new Float32Array([22, 28, 49, 64]), + rawInputA: new Float32Array([1, 2, 4, 5, 3, 0, 6, 0]), + rawInputB: new Float32Array([1, 2, 3, 4, 5, 6, 0, 0]), + }, + { + elementCountA: 6, + elementCountB: 6, + inputShapeA: [2, 3], + inputShapeB: [3, 2], + outputShape: [2, 2], + inputTextureShapeA: [2, 1], + inputTextureShapeB: [1, 2], + outputTextureShape: [1, 1], + expectedOutput: new Float32Array([23, 29, 50, 65]), + biasValue: 1, + rawInputA: new Float32Array([1, 2, 4, 5, 3, 0, 6, 0]), + rawInputB: new Float32Array([1, 2, 3, 4, 5, 6, 0, 0]), + }, + { + elementCountA: 12, + elementCountB: 12, + inputShapeA: [2, 2, 3], + inputShapeB: [2, 3, 2], + outputShape: [2, 2, 2], + inputTextureShapeA: [2, 2], + inputTextureShapeB: [1, 4], + outputTextureShape: [2, 1], + expectedOutput: new Float32Array([23, 29, 50, 65, 23, 29, 50, 65]), + biasValue: 1, + rawInputA: new Float32Array([1, 2, 4, 5, 3, 0, 6, 0, 1, 2, 4, 5, 3, 0, 6, 0]), + rawInputB: new Float32Array([1, 2, 3, 4, 5, 6, 0, 0, 1, 2, 3, 4, 5, 6, 0, 0]), + }, + // test bcast + { + elementCountA: 12, + elementCountB: 6, + inputShapeA: [2, 2, 3], + inputShapeB: [3, 2], + outputShape: [2, 2, 2], + inputTextureShapeA: [2, 2], + inputTextureShapeB: [1, 2], + outputTextureShape: [2, 1], + expectedOutput: new Float32Array([23, 29, 50, 65, 23, 29, 50, 65]), + biasValue: 1, + rawInputA: new Float32Array([1, 2, 4, 5, 3, 0, 6, 0, 1, 2, 4, 5, 3, 0, 6, 0]), + rawInputB: new Float32Array([1, 2, 3, 4, 5, 6, 0, 0]), + }, + ]; +} diff --git a/test/unittests/backends/webgl/test_pack_unpack.ts b/test/unittests/backends/webgl/test_pack_unpack.ts index cee318ed..ec6867a1 100644 --- a/test/unittests/backends/webgl/test_pack_unpack.ts +++ b/test/unittests/backends/webgl/test_pack_unpack.ts @@ -87,6 +87,7 @@ describe('#UnitTest# - pack - Tensor pack', () => { const outputElementCount = getExpectedElementCount(testData.inputShape); expect(resultDataBuffer).to.have.lengthOf(outputElementCount); const expectedOutput = generateExpected(inputData, testData.inputShape); + expect(resultDataBuffer).to.deep.equal(expectedOutput); }); } @@ -171,6 +172,7 @@ describe('#UnitTest# - unpack - Tensor unpack', () => { const outputElementCount = getExpectedElementCount(testData.inputShape); expect(resultDataBuffer).to.have.lengthOf(outputElementCount); + expect(result).to.deep.equal(expectedOutput); }); } @@ -293,6 +295,21 @@ function getTestData(isPacked = true): TestData[] { inputTextureShape: [], outputTextureShape: [24, 40] }, + // test 6D tensor + { + elementCount: 32, + inputShape: [1, 1, 2, 2, 2, 4], + outputShape: [], + inputTextureShape: [], + outputTextureShape: [4, 2] + }, + { + elementCount: 3840, + inputShape: [1, 1, 2, 24, 2, 40], + outputShape: [], + inputTextureShape: [], + outputTextureShape: [48, 20] + }, ]; } else { return [ @@ -376,6 +393,23 @@ function getTestData(isPacked = true): TestData[] { outputTextureShape: [16, 4], useGeneratedOutput: true, }, + // test 6d tensor + { + elementCount: 32, + inputShape: [1, 1, 2, 2, 2, 4], + outputShape: [1, 1, 2, 2, 2, 4], + inputTextureShape: [2, 4], + outputTextureShape: [8, 4], + useGeneratedOutput: true, + }, + { + elementCount: 64, + inputShape: [1, 2, 1, 2, 4, 4], + outputShape: [1, 2, 1, 2, 4, 4], + inputTextureShape: [2, 8], + outputTextureShape: [16, 4], + useGeneratedOutput: true, + }, ]; } } diff --git a/test/unittests/backends/webgl/test_utils.ts b/test/unittests/backends/webgl/test_utils.ts index 15de40bd..d88af1be 100644 --- a/test/unittests/backends/webgl/test_utils.ts +++ b/test/unittests/backends/webgl/test_utils.ts @@ -6,6 +6,16 @@ export function createAscendingArray(size: number): Float32Array { return new Float32Array(Array.from({length: size}, (v, i) => (i + 1))); } +// Returns an array by injecting 3 zeros after every element in the input array to be used for creating unpacked +// texture. +export function generateArrayForUnpackedTexture(input: Float32Array): Float32Array { + const output = new Float32Array(input.length * 4); + for (let i = 0; i < (input.length * 4); i += 4) { + output[i] = input[i / 4]; + } + return output; +} + // create a webgl texture and fill it with the array content export function createTextureFromArray( glContext: WebGLContext, dataArray: Float32Array, type: GLenum, width: number, height: number) { diff --git a/test/unittests/index.ts b/test/unittests/index.ts index 539cbb75..3c26b870 100644 --- a/test/unittests/index.ts +++ b/test/unittests/index.ts @@ -8,7 +8,9 @@ if (typeof window !== 'undefined' && !onnx.backend.webgl.disabled) { require('./backends/webgl/test_conv_new'); require('./backends/webgl/test_pack_unpack'); require('./backends/webgl/test_concat_packed'); + require('./backends/webgl/test_depth_to_space'); require('./backends/webgl/test_reshape_packed'); + require('./backends/webgl/test_matmul_packed'); } // require('./api/onnx');