mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-16 21:00:14 +00:00
4385602386
### Description fix test runner with optional input/output. This change fixes the OP test runner (.jsonc format test) with optional input(s) and/or output(s). this fix reveals a problem of dealing with optional outputs: > Take SkipSimplifiedLayerNorm as example: > > if in the ONNX model, the node's outputs are: [ 'output_0', '' ] instead of [ 'output_0' ], the current implementation will fail. The difference is, in the first case, context.outputCount == 2, and then the typescript implementation will try to create a tensor for output[1]. It will eventually call to C++ function (OpKernelContext::Output), and the output.DataRaw() will be nullptr. WebGPU backend will fail because it cannot deal with a TensorView with data == 0. > This problem may need to be fixed or workaround in separated PR. This PR does not fix this problem. Failed test cases are modified to work - please note this PR does not break those test cases as they never work.
1005 lines
39 KiB
TypeScript
1005 lines
39 KiB
TypeScript
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import {expect} from 'chai';
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import * as ort from 'onnxruntime-common';
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import {extname} from 'path';
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import {inspect} from 'util';
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import {Attribute} from '../lib/onnxjs/attribute';
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import {InferenceHandler, resolveBackend, SessionHandler} from '../lib/onnxjs/backend';
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import {createWebGLContext} from '../lib/onnxjs/backends/webgl/webgl-context-factory';
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import {Logger, Profiler} from '../lib/onnxjs/instrument';
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import {Operator} from '../lib/onnxjs/operators';
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import {onnx} from '../lib/onnxjs/ort-schema/protobuf/onnx';
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import {Tensor} from '../lib/onnxjs/tensor';
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import {ProtoUtil} from '../lib/onnxjs/util';
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import {createView} from '../lib/wasm/jsep/tensor-view';
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import {getTensorElementSize, isGpuBufferSupportedType, tensorDataTypeStringToEnum} from '../lib/wasm/wasm-common';
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import {base64toBuffer, createMockGraph, readFile} from './test-shared';
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import {Test} from './test-types';
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// the threshold that used to compare 2 float numbers. See above for TensorResultValidator.floatEqual().
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const CPU_THRESHOLD_ABSOLUTE_ERROR = 1.0e-4;
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const CPU_THRESHOLD_RELATIVE_ERROR = 1.000001;
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const WEBGL_THRESHOLD_ABSOLUTE_ERROR = 1.0e-3;
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const WEBGL_THRESHOLD_RELATIVE_ERROR = 1.00001;
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const WEBGL_HALF_FLOAT_THRESHOLD_ABSOLUTE_ERROR = 0.1;
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const WEBGL_HALF_FLOAT_THRESHOLD_RELATIVE_ERROR = 1.02;
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const WEBGPU_THRESHOLD_ABSOLUTE_ERROR = 1.0e-3;
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const WEBGPU_THRESHOLD_RELATIVE_ERROR = 1.00001;
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const WASM_THRESHOLD_ABSOLUTE_ERROR = 1.0e-4;
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const WASM_THRESHOLD_RELATIVE_ERROR = 1.000001;
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const ONNXRUNTIME_THRESHOLD_ABSOLUTE_ERROR = 1.0e-3;
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const ONNXRUNTIME_THRESHOLD_RELATIVE_ERROR = 1.00001;
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/**
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* returns a number to represent the current timestamp in a resolution as high as possible.
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*/
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const now = (typeof performance !== 'undefined' && performance.now) ? () => performance.now() : Date.now;
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function fromInternalTensor(tensor: Tensor): ort.Tensor {
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return new ort.Tensor(tensor.type, tensor.data as ort.Tensor.DataType, tensor.dims);
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}
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async function loadTensorProto(uriOrData: string|Uint8Array, allowInt64 = false): Promise<Test.NamedTensor> {
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const buf = (typeof uriOrData === 'string') ? await readFile(uriOrData) : uriOrData;
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const tensorProto = onnx.TensorProto.decode(buf);
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let tensor: ort.Tensor;
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// by default, we don't allow (u)int64. this is for backward compatibility.
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if (allowInt64 && tensorProto && tensorProto.dataType &&
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((tensorProto.dataType === onnx.TensorProto.DataType.INT64 ||
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tensorProto.dataType === onnx.TensorProto.DataType.UINT64))) {
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const signed = tensorProto.dataType === onnx.TensorProto.DataType.INT64;
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const dataConstructor = signed ? BigInt64Array : BigUint64Array;
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const length = tensorProto.rawData.byteLength / 8;
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const data = new dataConstructor(length);
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if (tensorProto.rawData && typeof tensorProto.rawData.byteLength === 'number' &&
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tensorProto.rawData.byteLength > 0) {
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const dataSource =
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new DataView(tensorProto.rawData.buffer, tensorProto.rawData.byteOffset, tensorProto.rawData.byteLength);
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for (let i = 0; i < length; i++) {
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data[i] = signed ? dataSource.getBigInt64(i * 8, true) : dataSource.getBigUint64(i * 8, true);
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}
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} else {
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for (let i = 0; i < length; i++) {
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data[i] = BigInt((signed ? tensorProto.int64Data : tensorProto.uint64Data)![i].toString());
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}
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}
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tensor = new ort.Tensor(signed ? 'int64' : 'uint64', data, ProtoUtil.tensorDimsFromProto(tensorProto.dims));
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} else {
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const internalTensor = Tensor.fromProto(tensorProto);
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tensor = fromInternalTensor(internalTensor);
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}
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// add property 'name' to the tensor object.
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const namedTensor = tensor as unknown as Test.NamedTensor;
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namedTensor.name = tensorProto.name;
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return namedTensor;
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}
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async function loadMlProto(_uriOrData: string|Uint8Array): Promise<Test.NamedTensor> {
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return Promise.reject('not supported');
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}
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async function loadTensors(
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modelMetaData: {inputNames: readonly string[]; outputNames: readonly string[]}, testCase: Test.ModelTestCase,
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backendName: string, fileCache?: FileCacheBuffer) {
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const inputs: Test.NamedTensor[] = [];
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const outputs: Test.NamedTensor[] = [];
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let dataFileType: 'none'|'pb'|'npy' = 'none';
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const allowInt64 = ['wasm', 'webgpu', 'webnn'].includes(backendName);
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for (const dataFile of testCase.dataFiles) {
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const ext = extname(dataFile);
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if (ext.toLowerCase() === '.pb' || ext.toLowerCase() === '.tpb') {
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if (dataFileType === 'none') {
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dataFileType = 'pb';
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}
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if (dataFileType !== 'pb') {
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throw new Error(`cannot load data from test case "${testCase.name}", multiple types of files detected`);
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}
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const uriOrData = fileCache && fileCache[dataFile] ? fileCache[dataFile] : dataFile;
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const t = ext.toLowerCase() === '.pb' ? await loadTensorProto(uriOrData, allowInt64) : // onnx.TensorProto
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await loadMlProto(uriOrData);
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const dataFileBasename = dataFile.split(/[/\\]/).pop()!;
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if (dataFileBasename.indexOf('input') !== -1) {
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inputs.push(t);
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} else if (dataFileBasename.indexOf('output') !== -1) {
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outputs.push(t);
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}
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} else {
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throw new Error(`${ext} file is not supported now`);
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}
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}
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// if model has single input/output, and tensor name is empty, we assign model's input/output names to it.
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if (modelMetaData.inputNames.length === 1 && inputs.length === 1 && !inputs[0].name) {
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inputs[0].name = modelMetaData.inputNames[0];
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}
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if (modelMetaData.outputNames.length === 1 && outputs.length === 1 && !outputs[0].name) {
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outputs[0].name = modelMetaData.outputNames[0];
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}
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testCase.inputs = inputs;
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testCase.outputs = outputs;
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}
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async function initializeSession(
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modelFilePath: string, backendHint: ort.InferenceSession.ExecutionProviderConfig, ioBindingMode: Test.IOBindingMode,
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profile: boolean, externalData: ort.InferenceSession.SessionOptions['externalData'],
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sessionOptions: ort.InferenceSession.SessionOptions, fileCache?: FileCacheBuffer): Promise<ort.InferenceSession> {
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const preloadModelData: Uint8Array|undefined =
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fileCache && fileCache[modelFilePath] ? fileCache[modelFilePath] : undefined;
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Logger.verbose(
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'TestRunner',
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`Start to load model from file: ${modelFilePath}${
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preloadModelData ? ` [preloaded(${preloadModelData.byteLength})]` : ''}`);
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const profilerConfig = profile ? {maxNumberEvents: 65536} : undefined;
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const sessionConfig = {
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...sessionOptions,
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executionProviders: [backendHint],
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profiler: profilerConfig,
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enableProfiling: profile,
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preferredOutputLocation: ioBindingMode === 'gpu-location' ? ('gpu-buffer' as const) : undefined,
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externalData
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};
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let session: ort.InferenceSession;
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try {
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if (preloadModelData) {
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session = await ort.InferenceSession.create(preloadModelData, sessionConfig);
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} else {
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const modelData = await readFile(modelFilePath);
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session = await ort.InferenceSession.create(modelData, sessionConfig);
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}
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} catch (e) {
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Logger.error(
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'TestRunner',
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`Failed to load model from file: ${modelFilePath}. ` +
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`Error: ${e.message} @ ${e.fileName}:${e.lineNumber}`);
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throw e;
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}
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if (profile) {
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session.startProfiling();
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}
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Logger.verbose('TestRunner', `Finished loading model from file: ${modelFilePath}`);
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return session;
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}
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type FileCacheBuffer = {
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[filePath: string]: Uint8Array;
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};
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/**
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* a ModelTestContext object contains all states in a ModelTest
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*/
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export class ModelTestContext {
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private constructor(
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readonly session: ort.InferenceSession,
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readonly backend: string,
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readonly perfData: ModelTestContext.ModelTestPerfData,
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readonly ioBinding: Test.IOBindingMode,
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private readonly profile: boolean,
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) {}
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/**
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* dump the current performance data
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*/
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private logPerfData() {
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const data = this.perfData;
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Logger.verbose('TestRunner.Perf', '***Perf Data Start');
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Logger.verbose('TestRunner.Perf', ` * Init : ${data.init}`);
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Logger.verbose('TestRunner.Perf', ` * Running times : ${data.count}`);
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Logger.verbose('TestRunner.Perf', ` * FirstRun : ${data.firstRun.toFixed(2)}`);
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const runs = data.runs;
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if (runs.length > 0) {
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Logger.verbose('TestRunner.Perf', ` * Runs : ${runs.map(r => r.toFixed(2)).join(', ')}`);
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if (runs.length > 1) {
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const sorted = runs.sort((a, b) => a - b);
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Logger.verbose('TestRunner.Perf', ` * Runs P50 : ${sorted[Math.floor((runs.length - 1) / 2)].toFixed(2)}`);
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const avg = runs.reduce((prev, current) => prev + current) / runs.length;
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Logger.verbose('TestRunner.Perf', ` * Runs Avg : ${avg.toFixed(2)}`);
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const variance = runs.reduce((prev, current) => prev + (current - avg) * (current - avg));
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const sd = Math.sqrt(variance / (runs.length - 1));
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Logger.verbose('TestRunner.Perf', ` * Runs SD : ${sd.toFixed(2)}`);
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}
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}
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Logger.verbose('TestRunner.Perf', '***Perf Data End');
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}
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async release(): Promise<void> {
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if (this.profile) {
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this.session.endProfiling();
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}
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this.logPerfData();
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await this.session.release();
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}
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/**
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* create a ModelTestContext object that used in every test cases in the given ModelTest.
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*/
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static async create(modelTest: Test.ModelTest, profile: boolean, testOptions?: Test.Options):
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Promise<ModelTestContext> {
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if (this.initializing) {
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throw new Error('cannot create a ModelTestContext object when the previous creation is not done');
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}
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try {
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this.initializing = true;
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const initStart = now();
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const executionProviderConfig =
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modelTest.backend === 'webnn' ? (testOptions?.webnnOptions || 'webnn') : modelTest.backend!;
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const session = await initializeSession(
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modelTest.modelUrl, executionProviderConfig, modelTest.ioBinding, profile, modelTest.externalData,
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testOptions?.sessionOptions || {}, this.cache);
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const initEnd = now();
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for (const testCase of modelTest.cases) {
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await loadTensors(session, testCase, modelTest.backend!, this.cache);
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}
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return new ModelTestContext(
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session,
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modelTest.backend!,
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{init: initEnd - initStart, firstRun: -1, runs: [], count: 0},
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modelTest.ioBinding,
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profile,
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);
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} finally {
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this.initializing = false;
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}
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}
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/**
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* set the global file cache for looking up model and tensor protobuf files.
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*/
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static setCache(cache: Test.FileCache): void {
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const keys = Object.keys(cache);
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Logger.info('TestRunner', `Setting up file cache... Entry count: ${keys.length}.`);
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for (const key of keys) {
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this.cache[key] = base64toBuffer(cache[key]);
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}
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}
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private static initializing = false;
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private static cache: FileCacheBuffer = {};
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}
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export declare namespace ModelTestContext {
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export interface ModelTestPerfData {
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init: number;
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firstRun: number;
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runs: number[];
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count: number;
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}
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}
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export class TensorResultValidator {
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private readonly absoluteThreshold: number;
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private readonly relativeThreshold: number;
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private readonly maxFloatValue: number = 3.4028234663852886e+38;
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private static isHalfFloat: boolean|undefined;
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constructor(backend: string) {
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if (backend === 'cpu') {
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this.absoluteThreshold = CPU_THRESHOLD_ABSOLUTE_ERROR;
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this.relativeThreshold = CPU_THRESHOLD_RELATIVE_ERROR;
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} else if (backend === 'webgl') {
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if (TensorResultValidator.isHalfFloat === undefined) {
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TensorResultValidator.isHalfFloat = !createWebGLContext(ort.env.webgl.contextId).isRenderFloat32Supported;
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}
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if (TensorResultValidator.isHalfFloat) {
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this.maxFloatValue = 65504;
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this.absoluteThreshold = WEBGL_HALF_FLOAT_THRESHOLD_ABSOLUTE_ERROR;
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this.relativeThreshold = WEBGL_HALF_FLOAT_THRESHOLD_RELATIVE_ERROR;
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} else {
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this.absoluteThreshold = WEBGL_THRESHOLD_ABSOLUTE_ERROR;
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this.relativeThreshold = WEBGL_THRESHOLD_RELATIVE_ERROR;
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}
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} else if (backend === 'webgpu') {
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this.absoluteThreshold = WEBGPU_THRESHOLD_ABSOLUTE_ERROR;
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this.relativeThreshold = WEBGPU_THRESHOLD_RELATIVE_ERROR;
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} else if (backend === 'wasm' || backend === 'webnn') {
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this.absoluteThreshold = WASM_THRESHOLD_ABSOLUTE_ERROR;
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this.relativeThreshold = WASM_THRESHOLD_RELATIVE_ERROR;
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} else if (backend === 'onnxruntime') {
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this.absoluteThreshold = ONNXRUNTIME_THRESHOLD_ABSOLUTE_ERROR;
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this.relativeThreshold = ONNXRUNTIME_THRESHOLD_RELATIVE_ERROR;
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} else {
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throw new Error(`backend not supported: ${backend}`);
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}
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}
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checkTensorResult(actual: Tensor[], expected: Tensor[]): void {
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this.checkApiTensorResult(actual.map(fromInternalTensor), expected.map(fromInternalTensor));
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}
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checkApiTensorResult(actual: ort.Tensor[], expected: ort.Tensor[]): void {
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// check output size
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expect(actual.length, 'size of output tensors').to.equal(expected.length);
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// compare output one-by-one
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for (let i = 0; i < actual.length; ++i) {
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const match = this.areEqual(actual[i], expected[i]);
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if (!match) {
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Logger.error(
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'TestRunner',
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`Tensor mismatch: \nACTUAL: type=${actual[i].type}; dims=[${actual[i].dims}]; data=[${
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actual[i].data}]\nEXPECT: type=${expected[i].type}; dims=[${expected[i].dims}]; data=[${
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expected[i].data}]`);
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}
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expect(match, 'tensor data should match').to.be.true;
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}
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}
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checkNamedTensorResult(actual: Record<string, ort.Tensor>, expected: Test.NamedTensor[]): void {
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// check output size
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expect(Object.getOwnPropertyNames(actual).length, 'size of output tensors').to.equal(expected.length);
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// check output mapping
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for (const expectedOneOutput of expected) {
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expect(actual, 'keys of output tensors').to.contain.keys(expectedOneOutput.name);
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}
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this.checkApiTensorResult(expected.map(i => actual[i.name]!), expected);
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}
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// This function check whether 2 tensors should be considered as 'match' or not
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areEqual(actual: ort.Tensor, expected: ort.Tensor): boolean {
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if (!actual || !expected) {
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return false;
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}
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if (!actual.dims || !expected.dims) {
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return false;
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}
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const actualDims = actual.dims;
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const actualType = actual.type;
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const expectedDims = expected.dims;
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const expectedType = expected.type;
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if (actualType !== expectedType) {
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return false;
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}
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if (actualDims.length !== expectedDims.length) {
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return false;
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}
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for (let i = 0; i < actualDims.length; i++) {
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if (actualDims[i] !== expectedDims[i]) {
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return false;
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}
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}
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switch (actualType) {
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case 'string':
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return this.strictEqual(actual.data, expected.data);
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case 'float32':
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case 'float64':
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return this.floatEqual(
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actual.data as number[] | Float32Array | Float64Array,
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expected.data as number[] | Float32Array | Float64Array);
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case 'uint8':
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case 'int8':
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case 'uint16':
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case 'int16':
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case 'int32':
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case 'uint32':
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case 'int64':
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case 'bool':
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return TensorResultValidator.integerEqual(
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actual.data as number[] | Uint8Array | Int8Array | Uint16Array | Int16Array | Uint32Array | Int32Array,
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expected.data as number[] | Uint8Array | Int8Array | Uint16Array | Int16Array | Uint32Array | Int32Array);
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default:
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throw new Error('type not implemented or not supported');
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}
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}
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strictEqual<T>(actual: T, expected: T): boolean {
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try {
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expect(actual).to.deep.equal(expected);
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return true;
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} catch {
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return false;
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}
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}
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floatEqual(actual: number[]|Float32Array|Float64Array, expected: number[]|Float32Array|Float64Array): boolean {
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if (actual.length !== expected.length) {
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return false;
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}
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for (let i = actual.length - 1; i >= 0; i--) {
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const a = actual[i];
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let b = expected[i];
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if (a === b) {
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continue; // exact the same value, treat as equal
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}
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// check for NaN
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//
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if (Number.isNaN(a) && Number.isNaN(b)) {
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continue; // 2 numbers are NaN, treat as equal
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}
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if (Number.isNaN(a) || Number.isNaN(b)) {
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Logger.error('Validator', `a or b isNan -- index:${i}: actual=${actual[i]},expected=${expected[i]}`);
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return false; // one is NaN and the other is not
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}
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// check for Infinity
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//
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if (!Number.isFinite(a) || !Number.isFinite(b)) {
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Logger.error('Validator', `a or b is Infinity -- index:${i}: actual=${actual[i]},expected=${expected[i]}`);
|
|
return false; // at least one is Infinity and the other is not or their sign is different
|
|
}
|
|
|
|
// normalize value of b
|
|
b = Math.max(Math.min(expected[i], this.maxFloatValue), -this.maxFloatValue);
|
|
|
|
// Comparing 2 float numbers: (Suppose a >= b)
|
|
//
|
|
// if ( a - b < ABSOLUTE_ERROR || 1.0 < a / b < RELATIVE_ERROR)
|
|
// test pass
|
|
// else
|
|
// test fail
|
|
// endif
|
|
//
|
|
if (Math.abs(actual[i] - expected[i]) < this.absoluteThreshold) {
|
|
continue; // absolute error check pass
|
|
}
|
|
if (a !== 0 && b !== 0 && a / b < this.relativeThreshold && b / a < this.relativeThreshold) {
|
|
continue; // relative error check pass
|
|
}
|
|
|
|
// if code goes here, it means both (abs/rel) check failed.
|
|
Logger.error('Validator', `abs/rel check failed-- index:${i}: actual=${actual[i]},expected=${expected[i]}`);
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
static integerEqual(
|
|
actual: number[]|Uint8Array|Int8Array|Uint16Array|Int16Array|Uint32Array|Int32Array,
|
|
expected: number[]|Uint8Array|Int8Array|Uint16Array|Int16Array|Uint32Array|Int32Array): boolean {
|
|
if (actual.length !== expected.length) {
|
|
return false;
|
|
}
|
|
|
|
for (let i = actual.length - 1; i >= 0; i--) {
|
|
if (actual[i] !== expected[i]) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
}
|
|
|
|
function createGpuTensorForInput(cpuTensor: ort.Tensor): ort.Tensor {
|
|
if (!isGpuBufferSupportedType(cpuTensor.type) || Array.isArray(cpuTensor.data)) {
|
|
throw new Error(`createGpuTensorForInput can not work with ${cpuTensor.type} tensor`);
|
|
}
|
|
const device = ort.env.webgpu.device as GPUDevice;
|
|
const gpuBuffer = device.createBuffer({
|
|
// eslint-disable-next-line no-bitwise
|
|
usage: GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE,
|
|
size: Math.ceil(cpuTensor.data.byteLength / 16) * 16,
|
|
mappedAtCreation: true
|
|
});
|
|
const arrayBuffer = gpuBuffer.getMappedRange();
|
|
new Uint8Array(arrayBuffer)
|
|
.set(new Uint8Array(cpuTensor.data.buffer, cpuTensor.data.byteOffset, cpuTensor.data.byteLength));
|
|
gpuBuffer.unmap();
|
|
|
|
// TODO: how to "await" for the copy to finish, so that we can get more accurate performance data?
|
|
|
|
return ort.Tensor.fromGpuBuffer(
|
|
gpuBuffer, {dataType: cpuTensor.type, dims: cpuTensor.dims, dispose: () => gpuBuffer.destroy()});
|
|
}
|
|
|
|
function createGpuTensorForOutput(type: ort.Tensor.Type, dims: readonly number[]) {
|
|
if (!isGpuBufferSupportedType(type)) {
|
|
throw new Error(`createGpuTensorForOutput can not work with ${type} tensor`);
|
|
}
|
|
|
|
const elementSizeInBytes = getTensorElementSize(tensorDataTypeStringToEnum(type))!;
|
|
const size = dims.reduce((a, b) => a * b, 1) * elementSizeInBytes;
|
|
|
|
const device = ort.env.webgpu.device as GPUDevice;
|
|
const gpuBuffer = device.createBuffer({
|
|
// eslint-disable-next-line no-bitwise
|
|
usage: GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE,
|
|
size: Math.ceil(size / 16) * 16
|
|
});
|
|
|
|
return ort.Tensor.fromGpuBuffer(gpuBuffer, {
|
|
dataType: type,
|
|
dims,
|
|
dispose: () => gpuBuffer.destroy(),
|
|
download: async () => {
|
|
const stagingBuffer = device.createBuffer({
|
|
// eslint-disable-next-line no-bitwise
|
|
usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST,
|
|
size: gpuBuffer.size
|
|
});
|
|
const encoder = device.createCommandEncoder();
|
|
encoder.copyBufferToBuffer(gpuBuffer, 0, stagingBuffer, 0, gpuBuffer.size);
|
|
device.queue.submit([encoder.finish()]);
|
|
|
|
await stagingBuffer.mapAsync(GPUMapMode.READ);
|
|
const arrayBuffer = stagingBuffer.getMappedRange().slice(0, size);
|
|
stagingBuffer.destroy();
|
|
|
|
return createView(arrayBuffer, type) as ort.Tensor.DataTypeMap[ort.Tensor.GpuBufferDataTypes];
|
|
}
|
|
});
|
|
}
|
|
|
|
export async function sessionRun(options: {
|
|
session: ort.InferenceSession; feeds: Record<string, ort.Tensor>;
|
|
outputsMetaInfo: Record<string, Pick<ort.Tensor, 'dims'|'type'>>;
|
|
ioBinding: Test.IOBindingMode;
|
|
}): Promise<[number, number, ort.InferenceSession.OnnxValueMapType]> {
|
|
const session = options.session;
|
|
const feeds = options.feeds;
|
|
const fetches: Record<string, ort.Tensor> = {};
|
|
|
|
// currently we only support IO Binding for WebGPU
|
|
//
|
|
// For inputs, we create GPU tensors on both 'gpu-tensor' and 'gpu-location' binding testing mode.
|
|
// For outputs, we create GPU tensors on 'gpu-tensor' binding testing mode only.
|
|
// in 'gpu-device' binding mode, outputs are not pre-allocated.
|
|
const shouldUploadInput = options.ioBinding === 'gpu-tensor' || options.ioBinding === 'gpu-location';
|
|
const shouldUploadOutput = options.ioBinding === 'gpu-tensor';
|
|
try {
|
|
if (shouldUploadInput) {
|
|
// replace the CPU tensors in feeds into GPU tensors
|
|
for (const name in feeds) {
|
|
if (Object.hasOwnProperty.call(feeds, name)) {
|
|
if (feeds[name].size > 0) {
|
|
feeds[name] = createGpuTensorForInput(feeds[name]);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (shouldUploadOutput) {
|
|
for (const name in options.outputsMetaInfo) {
|
|
if (Object.hasOwnProperty.call(options.outputsMetaInfo, name)) {
|
|
const {type, dims} = options.outputsMetaInfo[name];
|
|
if (dims.some(d => d === 0)) {
|
|
fetches[name] = new ort.Tensor(type, [], dims);
|
|
} else {
|
|
fetches[name] = createGpuTensorForOutput(type, dims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
const start = now();
|
|
Logger.verbose('TestRunner', `Timestamp before session run: ${start}`);
|
|
const outputs = await (
|
|
shouldUploadOutput ? session.run(feeds, fetches) :
|
|
session.run(feeds, Object.getOwnPropertyNames(options.outputsMetaInfo)));
|
|
const end = now();
|
|
Logger.verbose('TestRunner', `Timestamp after session run: ${end}`);
|
|
|
|
// download each output tensor if needed
|
|
for (const name in outputs) {
|
|
if (Object.hasOwnProperty.call(outputs, name)) {
|
|
const tensor = outputs[name];
|
|
// Tensor.getData(true) release the underlying resource
|
|
await tensor.getData(true);
|
|
}
|
|
}
|
|
|
|
return [start, end, outputs];
|
|
} finally {
|
|
// dispose the GPU tensors in feeds
|
|
for (const name in feeds) {
|
|
if (Object.hasOwnProperty.call(feeds, name)) {
|
|
const tensor = feeds[name];
|
|
tensor.dispose();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/**
|
|
* run a single model test case. the inputs/outputs tensors should already been prepared.
|
|
*/
|
|
export async function runModelTestSet(
|
|
context: ModelTestContext, testCase: Test.ModelTestCase, testName: string): Promise<void> {
|
|
Logger.verbose('TestRunner', `Start to run test data from folder: ${testName}/${testCase.name}`);
|
|
Logger.verbose('TestRunner', `Start to run test data from folder: ${testCase.name}`);
|
|
const validator = new TensorResultValidator(context.backend);
|
|
try {
|
|
const feeds: Record<string, ort.Tensor> = {};
|
|
const outputsMetaInfo: Record<string, ort.Tensor> = {};
|
|
testCase.inputs!.forEach((tensor) => feeds[tensor.name] = tensor);
|
|
testCase.outputs!.forEach((tensor) => outputsMetaInfo[tensor.name] = tensor);
|
|
const [start, end, outputs] =
|
|
await sessionRun({session: context.session, feeds, outputsMetaInfo, ioBinding: context.ioBinding});
|
|
if (context.perfData.count === 0) {
|
|
context.perfData.firstRun = end - start;
|
|
} else {
|
|
context.perfData.runs.push(end - start);
|
|
}
|
|
context.perfData.count++;
|
|
|
|
Logger.verbose('TestRunner', `Finished running model from file: ${testCase.name}`);
|
|
Logger.verbose('TestRunner', ' Stats:');
|
|
Logger.verbose('TestRunner', ` Input(s): ${testCase.inputs!.length}`);
|
|
testCase.inputs!.forEach(i => {
|
|
Logger.verbose('TestRunner', ` '${i.name}': ${i.type}[${i.dims.join(',')}]`);
|
|
});
|
|
Logger.verbose('TestRunner', ` Output(s): ${Object.keys(outputs).length}`);
|
|
for (const name in outputs) {
|
|
if (Object.hasOwnProperty.call(outputs, name)) {
|
|
const tensor = outputs[name];
|
|
Logger.verbose('TestRunner', ` '${name}': ${tensor.type}[${tensor.dims.join(',')}]`);
|
|
}
|
|
}
|
|
|
|
validator.checkNamedTensorResult(outputs, testCase.outputs!);
|
|
|
|
Logger.verbose('TestRunner', ' Result: PASS');
|
|
} catch (e) {
|
|
Logger.error('TestRunner', ' Result: FAILED');
|
|
Logger.error('TestRunner', `Failed to run test data from folder: ${testCase.name}. Error: ${inspect(e)}`);
|
|
throw e;
|
|
}
|
|
}
|
|
|
|
function initializeOperator(
|
|
sessionHandler: SessionHandler, opType: string, attributeValues: readonly Test.AttributeValue[],
|
|
opsetImports: readonly Test.OperatorTestOpsetImport[]): Operator {
|
|
const attributes = new Attribute(undefined);
|
|
attributeValues.forEach(value => attributes.set(value.name, value.type, value.data));
|
|
const graph = createMockGraph(opType, attributes);
|
|
return sessionHandler.resolve(graph.getNodes()[0], opsetImports, graph);
|
|
}
|
|
|
|
/**
|
|
* a OpTestContext object contains all states in a OpTest. used for webgl backend.
|
|
*/
|
|
export class OpTestContext {
|
|
static profiler = Profiler.create();
|
|
|
|
readonly backendHint: string;
|
|
sessionHandler: SessionHandler;
|
|
inferenceHandler: InferenceHandler;
|
|
|
|
constructor(protected opTest: Test.OperatorTest) {
|
|
this.backendHint = opTest.backend ?? 'cpu';
|
|
}
|
|
createOperator(): Operator {
|
|
return initializeOperator(
|
|
this.sessionHandler, this.opTest.operator, this.opTest.attributes || [],
|
|
[this.opTest.opset ?? {domain: '', version: 7}]);
|
|
}
|
|
|
|
async dispose(): Promise<void> {
|
|
this.inferenceHandler.dispose();
|
|
this.sessionHandler.dispose();
|
|
}
|
|
|
|
async init(): Promise<void> {
|
|
const backend = await resolveBackend(this.backendHint);
|
|
this.sessionHandler = backend.createSessionHandler({profiler: OpTestContext.profiler});
|
|
this.inferenceHandler = this.sessionHandler.createInferenceHandler();
|
|
}
|
|
}
|
|
|
|
/**
|
|
* a ProtoOpTestContext uses a protobuf model for operator test. used for ORT based backend.
|
|
*/
|
|
export class ProtoOpTestContext {
|
|
private readonly loadedData: Uint8Array; // model data, inputs, outputs
|
|
session: ort.InferenceSession;
|
|
readonly backendHint: string;
|
|
readonly ioBindingMode: Test.IOBindingMode;
|
|
constructor(test: Test.OperatorTest, private readonly sessionOptions: ort.InferenceSession.SessionOptions = {}) {
|
|
const opsetImport = onnx.OperatorSetIdProto.create(test.opset);
|
|
const operator = test.operator;
|
|
const attribute = (test.attributes || []).map(attr => {
|
|
const protoAttr = onnx.AttributeProto.create({name: attr.name});
|
|
switch (attr.type) {
|
|
case 'float':
|
|
protoAttr.type = onnx.AttributeProto.AttributeType.FLOAT;
|
|
protoAttr.f = attr.data as number;
|
|
break;
|
|
case 'int':
|
|
protoAttr.type = onnx.AttributeProto.AttributeType.INT;
|
|
protoAttr.i = attr.data as number;
|
|
break;
|
|
case 'string':
|
|
protoAttr.type = onnx.AttributeProto.AttributeType.STRING;
|
|
protoAttr.s = new TextEncoder().encode(attr.data as string);
|
|
break;
|
|
case 'floats':
|
|
protoAttr.type = onnx.AttributeProto.AttributeType.FLOATS;
|
|
protoAttr.floats = attr.data as number[];
|
|
break;
|
|
case 'ints':
|
|
protoAttr.type = onnx.AttributeProto.AttributeType.INTS;
|
|
protoAttr.ints = attr.data as number[];
|
|
break;
|
|
case 'strings':
|
|
protoAttr.type = onnx.AttributeProto.AttributeType.STRINGS;
|
|
protoAttr.strings = (attr.data as string[]).map(s => new TextEncoder().encode(s));
|
|
break;
|
|
default:
|
|
throw new Error(`Unsupported attribute type: ${attr.type}`);
|
|
}
|
|
return protoAttr;
|
|
});
|
|
|
|
if (test.cases.length === 0) {
|
|
throw new Error(`No test cases found for test: ${test.name} [${test.operator}]`);
|
|
}
|
|
const inputCount = test.cases[0].inputs!.length;
|
|
const outputCount = test.cases[0].outputs!.length;
|
|
if (test.cases.some(
|
|
testCase => testCase.inputs!.length !== inputCount || testCase.outputs!.length !== outputCount)) {
|
|
throw new Error(
|
|
`Test cases for test: ${test.name} [${test.operator}] must have the same number of inputs and outputs`);
|
|
}
|
|
const inputsOmitted = test.cases[0].inputs.map(input => !input.data);
|
|
const outputsOmitted = test.cases[0].outputs.map(output => !output.data);
|
|
for (let caseIndex = 1; caseIndex < test.cases.length; caseIndex++) {
|
|
const testCase = test.cases[caseIndex];
|
|
for (let i = 0; i < inputCount; i++) {
|
|
if (inputsOmitted[i] !== !testCase.inputs![i].data) {
|
|
throw new Error(`Test cases for test: ${test.name} [${
|
|
test.operator}] must have consistent inputs data availability. Data of input[${i}] in testCase #0 and #${
|
|
caseIndex} should be both available or both omitted.`);
|
|
}
|
|
}
|
|
for (let i = 0; i < outputCount; i++) {
|
|
if (outputsOmitted[i] !== !testCase.outputs![i].data) {
|
|
throw new Error(`Test cases for test: ${test.name} [${
|
|
test.operator}] must have consistent outputs data availability. Data of output[${
|
|
i}] in testCase #0 and #${caseIndex} should be both available or both omitted.`);
|
|
}
|
|
}
|
|
}
|
|
|
|
const model = onnx.ModelProto.create();
|
|
model.irVersion = onnx.Version.IR_VERSION;
|
|
model.opsetImport.push(opsetImport);
|
|
model.graph = onnx.GraphProto.create();
|
|
|
|
model.graph.node = [onnx.NodeProto.create({
|
|
input: test.cases[0].inputs!.map((t, i) => t.data ? `input_${i}` : ''),
|
|
output: test.cases[0].outputs!.map((t, i) => t.data ? `output_${i}` : ''),
|
|
opType: operator,
|
|
domain: test.opset?.domain,
|
|
name: operator,
|
|
attribute
|
|
})];
|
|
|
|
// normalize input shape definitions
|
|
let normalizedInputShapeDefinitions: ReadonlyArray<Test.InputShapeDefinition|undefined>;
|
|
if (!test.inputShapeDefinitions || test.inputShapeDefinitions === 'none') {
|
|
// if inputShapeDefinitions is not specified, use undefined for all inputs
|
|
normalizedInputShapeDefinitions = new Array(inputCount).fill(undefined);
|
|
} else if (test.inputShapeDefinitions === 'rankOnly') {
|
|
// check if all test cases have data
|
|
if (test.cases.some(testCase => testCase.inputs!.some(input => !input.data || !input.dims))) {
|
|
throw new Error(`Test cases for test: ${test.name} [${
|
|
test.operator}] must have data for each inputs when inputShapeDefinitions is 'rankOnly'`);
|
|
}
|
|
|
|
// if inputShapeDefinitions is 'rankOnly', use semantic names for all inputs. This means only rank is specified.
|
|
normalizedInputShapeDefinitions =
|
|
test.cases[0].inputs!.map((input: Test.TensorValue, i) => input.dims.map((_, j) => `_input_${i}_d${j}`));
|
|
|
|
// check if all test cases have the same rank for each inputs
|
|
if (test.cases.some(
|
|
testCase => testCase.inputs!.some(
|
|
(input: Test.TensorValue, i) =>
|
|
input.dims.length !== (test.cases[0].inputs![i] as Test.TensorValue).dims.length))) {
|
|
throw new Error(`Test cases for test: ${test.name} [${
|
|
test.operator}] must have the same rank for each inputs in different test cases`);
|
|
}
|
|
} else if (test.inputShapeDefinitions === 'static') {
|
|
// check if all test cases have data
|
|
if (test.cases.some(testCase => testCase.inputs!.some(input => !input.data || !input.dims))) {
|
|
throw new Error(`Test cases for test: ${test.name} [${
|
|
test.operator}] must have data for each inputs when inputShapeDefinitions is 'rankOnly'`);
|
|
}
|
|
|
|
// if inputShapeDefinitions is 'static', use the shape of the first test case for all inputs.
|
|
normalizedInputShapeDefinitions = test.cases[0].inputs!.map((input: Test.TensorValue) => input.dims);
|
|
|
|
// check if all test cases have the same shape for each inputs
|
|
if (test.cases.some(
|
|
testCase => testCase.inputs!.some(
|
|
(input: Test.TensorValue, i) => TensorResultValidator.integerEqual(
|
|
input.dims, (test.cases[0].inputs![i] as Test.TensorValue).dims)))) {
|
|
throw new Error(`Test cases for test: ${test.name} [${
|
|
test.operator}] must have the same shape for each inputs in different test cases`);
|
|
}
|
|
} else {
|
|
// if inputShapeDefinitions is specified as an array, use it as is.
|
|
// check if inputShapeDefinitions has the same number of inputs as test cases
|
|
if (test.inputShapeDefinitions && test.inputShapeDefinitions.length !== inputCount) {
|
|
throw new Error(
|
|
`Input shape definitions for test: ${test.name} [${test.operator}] must have the same number of inputs`);
|
|
}
|
|
normalizedInputShapeDefinitions = test.inputShapeDefinitions;
|
|
}
|
|
|
|
model.graph.input =
|
|
test.cases[0]
|
|
.inputs!
|
|
.map((input, i) => {
|
|
const shapeDefinition = normalizedInputShapeDefinitions[i];
|
|
const shape = shapeDefinition ? onnx.TensorShapeProto.create({
|
|
dim: shapeDefinition.map(
|
|
dim => onnx.TensorShapeProto.Dimension.create(
|
|
typeof dim === 'string' ? {dimParam: dim} : {dimValue: dim}))
|
|
}) :
|
|
undefined;
|
|
return onnx.ValueInfoProto.create({
|
|
name: `input_${i}`,
|
|
type: onnx.TypeProto.create({
|
|
tensorType: onnx.TypeProto.Tensor.create({elemType: tensorDataTypeStringToEnum(input.type), shape}),
|
|
}),
|
|
});
|
|
})
|
|
.filter((_, i) => test.cases[0].inputs![i].data);
|
|
|
|
model.graph.output =
|
|
test.cases[0]
|
|
.outputs!
|
|
.map((output, i) => onnx.ValueInfoProto.create({
|
|
name: `output_${i}`,
|
|
type: onnx.TypeProto.create({
|
|
tensorType: onnx.TypeProto.Tensor.create({elemType: tensorDataTypeStringToEnum(output.type)}),
|
|
}),
|
|
}))
|
|
.filter((_, i) => test.cases[0].outputs![i].data);
|
|
|
|
model.graph.name = test.name;
|
|
|
|
this.backendHint = test.backend!;
|
|
this.ioBindingMode = test.ioBinding;
|
|
this.loadedData = onnx.ModelProto.encode(model).finish().slice();
|
|
|
|
// in debug mode, open a new tab in browser for the generated onnx model.
|
|
if (ort.env.debug) {
|
|
const modelFile =
|
|
new File([this.loadedData], `op_test_generated_model_${test.name}.onnx`, {type: 'application/octet-stream'});
|
|
const modelTempUrl = URL.createObjectURL(modelFile);
|
|
const a = document.createElement('a');
|
|
a.href = modelTempUrl;
|
|
a.download = modelFile.name;
|
|
a.target = '_blank';
|
|
a.click();
|
|
URL.revokeObjectURL(modelTempUrl);
|
|
}
|
|
}
|
|
async init(): Promise<void> {
|
|
this.session = await ort.InferenceSession.create(this.loadedData, {
|
|
executionProviders: [this.backendHint],
|
|
preferredOutputLocation: this.ioBindingMode === 'gpu-location' ? ('gpu-buffer' as const) : undefined,
|
|
...this.sessionOptions
|
|
});
|
|
}
|
|
|
|
async dispose(): Promise<void> {
|
|
await this.session.release();
|
|
}
|
|
}
|
|
|
|
async function runProtoOpTestcase(
|
|
session: ort.InferenceSession, testCase: Test.OperatorTestCase, ioBindingMode: Test.IOBindingMode,
|
|
validator: TensorResultValidator): Promise<void> {
|
|
const feeds: Record<string, ort.Tensor> = {};
|
|
const fetches: Record<string, Pick<ort.Tensor, 'dims'|'type'>> = {};
|
|
testCase.inputs.forEach((input, i) => {
|
|
if (input.data) {
|
|
let data: number[]|BigUint64Array|BigInt64Array = input.data;
|
|
if (input.type === 'uint64') {
|
|
data = BigUint64Array.from(input.data.map(BigInt));
|
|
} else if (input.type === 'int64') {
|
|
data = BigInt64Array.from(input.data.map(BigInt));
|
|
}
|
|
feeds[`input_${i}`] = new ort.Tensor(input.type, data, input.dims);
|
|
}
|
|
});
|
|
|
|
const outputs: ort.Tensor[] = [];
|
|
const expectedOutputNames: string[] = [];
|
|
testCase.outputs.forEach((output, i) => {
|
|
if (output.data) {
|
|
let data: number[]|BigUint64Array|BigInt64Array = output.data;
|
|
if (output.type === 'uint64') {
|
|
data = BigUint64Array.from(output.data.map(BigInt));
|
|
} else if (output.type === 'int64') {
|
|
data = BigInt64Array.from(output.data.map(BigInt));
|
|
}
|
|
outputs.push(new ort.Tensor(output.type, data, output.dims));
|
|
expectedOutputNames.push(`output_${i}`);
|
|
fetches[`output_${i}`] = {dims: output.dims, type: output.type};
|
|
}
|
|
});
|
|
|
|
const [, , results] = await sessionRun({session, feeds, outputsMetaInfo: fetches, ioBinding: ioBindingMode});
|
|
|
|
const actualOutputNames = Object.getOwnPropertyNames(results);
|
|
expect(actualOutputNames.length).to.equal(expectedOutputNames.length);
|
|
expect(actualOutputNames).to.have.members(expectedOutputNames);
|
|
|
|
const actualOutputs = actualOutputNames.map(name => results[name]);
|
|
validator.checkApiTensorResult(actualOutputs, outputs);
|
|
}
|
|
|
|
function createTensor(dims: number[], type: Tensor.DataType, data: number[]): Tensor {
|
|
const tensor = new Tensor(dims, type);
|
|
for (let i = 0; i < data.length; ++i) {
|
|
tensor.data[i] = data[i];
|
|
}
|
|
return tensor;
|
|
}
|
|
|
|
async function runOpTestcase(
|
|
inferenceHandler: InferenceHandler, operator: Operator, testcase: Test.OperatorTestCase,
|
|
validator: TensorResultValidator): Promise<void> {
|
|
testcase.inputs.forEach((input: Test.TensorValue, i) => {
|
|
Logger.verbose('TestOpRunner', ` Input '${i}': ${input.type}[${input.dims.join(',')}]`);
|
|
});
|
|
const inputTensors = testcase.inputs.map(
|
|
(input: Test.TensorValue) => createTensor(input.dims, input.type as Tensor.DataType, input.data));
|
|
|
|
const results = operator.impl(inferenceHandler, inputTensors, operator.context);
|
|
|
|
// try async data read.
|
|
for (const result of results) {
|
|
try {
|
|
await result.getData();
|
|
} catch {
|
|
}
|
|
}
|
|
|
|
results.forEach((output, i) => {
|
|
Logger.verbose('TestOpRunner', ` Result'${i}': ${output.type}[${output.dims.join(',')}]`);
|
|
});
|
|
const expectedTensors = testcase.outputs.map(
|
|
(output: Test.TensorValue) => createTensor(output.dims, output.type as Tensor.DataType, output.data));
|
|
validator.checkTensorResult(results, expectedTensors);
|
|
}
|
|
|
|
/**
|
|
* run a single operator test case.
|
|
*/
|
|
export async function runOpTest(
|
|
testcase: Test.OperatorTestCase, context: ProtoOpTestContext|OpTestContext): Promise<void> {
|
|
if (context instanceof ProtoOpTestContext) {
|
|
await runProtoOpTestcase(
|
|
context.session, testcase, context.ioBindingMode, new TensorResultValidator(context.backendHint));
|
|
} else {
|
|
await runOpTestcase(
|
|
context.inferenceHandler, context.createOperator(), testcase, new TensorResultValidator(context.backendHint));
|
|
}
|
|
}
|