chore: update node_modules with new binary files and dependencies
- Add new binary files for nodemon, onnxruntime-web, and xenova/transformers - Update various JavaScript and TypeScript files in node_modules - Remove unused files and dependencies - Add new test fixtures and documentation files
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node_modules/onnxruntime-web/lib/onnxjs/backends/webgl/ops/softmax.ts
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node_modules/onnxruntime-web/lib/onnxjs/backends/webgl/ops/softmax.ts
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';
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import {Graph} from '../../../graph';
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import {OperatorImplementation, OperatorInitialization} from '../../../operators';
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import {Tensor} from '../../../tensor';
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import {ShapeUtil} from '../../../util';
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import {getGlsl} from '../glsl-source';
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import {WebGLInferenceHandler} from '../inference-handler';
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import {ProgramInfo, TextureType} from '../types';
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import {transpose, TransposeAttributes} from './transpose';
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export interface SoftmaxAttributes extends AttributeWithCacheKey {
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readonly axis: number;
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}
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const softmaxComputeMaxProgramMetadata = {
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name: 'SoftmaxComputeMax',
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inputNames: ['A'],
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inputTypes: [TextureType.unpacked],
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};
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const softmaxComputeScaleProgramMetadata = {
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name: 'SoftmaxComputeScale',
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inputNames: ['A', 'Max'],
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inputTypes: [TextureType.unpacked, TextureType.unpacked],
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};
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const softmaxProgramMetadata = {
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name: 'SoftMax',
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inputNames: ['A', 'Max', 'Norm'],
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inputTypes: [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked],
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};
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export const softmax: OperatorImplementation<SoftmaxAttributes> =
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(inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes): Tensor[] => {
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validateInputs(inputs);
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const inputShape = inputs[0].dims.slice();
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const axis = ShapeUtil.normalizeAxis(attributes.axis, inputShape.length);
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const logicalRowCount = ShapeUtil.sizeToDimension(inputShape, axis);
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const featureCount = ShapeUtil.sizeFromDimension(inputShape, axis);
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const output = computeSoftmax(inferenceHandler, inputs, attributes, logicalRowCount, featureCount);
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return output;
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};
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export const parseSoftmaxAttributes: OperatorInitialization<SoftmaxAttributes> =
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(node: Graph.Node): SoftmaxAttributes => createAttributeWithCacheKey({axis: node.attributes.getInt('axis', 1)});
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export const parseSoftmaxAttributesV13: OperatorInitialization<SoftmaxAttributes> =
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(node: Graph.Node): SoftmaxAttributes => createAttributeWithCacheKey({axis: node.attributes.getInt('axis', -1)});
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// The "semantic" meaning of axis has changed in opset-13.
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// Please compare: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Softmax
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// with https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Softmax-11 for detailed explanations
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// To account for the opset-13 behavior, our plan will be to transpose the "axis" dim to the innermost dim
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// and perform softmax and then reverse the transpose. We can skip the transposing aspect if the axis is already
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// the innermost dim
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export const softmaxV13: OperatorImplementation<SoftmaxAttributes> =
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(inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes): Tensor[] => {
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validateInputs(inputs);
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const inputShape = inputs[0].dims.slice();
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const axis = ShapeUtil.normalizeAxis(attributes.axis, inputShape.length);
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const rank = inputShape.length;
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const isTransposeRequired = (axis !== rank - 1) ? true : false;
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const transposedInputShape: number[] = [];
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let perm: number[] = [];
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let transposedInputs: Tensor[] = [];
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let transposeAttribute: TransposeAttributes;
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if (isTransposeRequired) {
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perm = Array.from({length: rank}).map((_, i) => i);
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// swap the innermost dim with the dim corresponding to axis
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perm[axis] = rank - 1;
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perm[rank - 1] = axis;
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perm.map(p => transposedInputShape.push(inputShape[p]));
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transposeAttribute = createAttributeWithCacheKey({perm});
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transposedInputs = transpose(inferenceHandler, inputs, transposeAttribute);
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}
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const logicalRowCount = isTransposeRequired ? ShapeUtil.sizeToDimension(transposedInputShape, rank - 1) :
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ShapeUtil.sizeToDimension(inputShape, rank - 1);
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const featureCount = isTransposeRequired ? ShapeUtil.sizeFromDimension(transposedInputShape, rank - 1) :
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ShapeUtil.sizeFromDimension(inputShape, rank - 1);
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const output = computeSoftmax(
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inferenceHandler, isTransposeRequired ? transposedInputs : inputs, attributes, logicalRowCount, featureCount);
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if (isTransposeRequired) {
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const reversedOutput = transpose(inferenceHandler, output, transposeAttribute!);
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return reversedOutput;
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} else {
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return output;
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}
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};
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const computeSoftmax =
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(inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes, logicalRowCount: number,
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featureCount: number): Tensor[] => {
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const computeMaxProgramInfo =
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createComputeMaxProgramInfo(inferenceHandler, inputs[0], logicalRowCount, featureCount, [logicalRowCount]);
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const max = inferenceHandler.run(
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{...softmaxComputeMaxProgramMetadata, cacheHint: attributes.cacheKey, get: () => computeMaxProgramInfo},
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inputs);
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const computeScaleProgramInfo = createComputScaleProgramInfo(
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inferenceHandler, inputs[0], logicalRowCount, featureCount, computeMaxProgramInfo.output.dims,
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[logicalRowCount]);
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const scale = inferenceHandler.run(
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{...softmaxComputeScaleProgramMetadata, cacheHint: attributes.cacheKey, get: () => computeScaleProgramInfo},
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[inputs[0], max]);
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const softMaxProgramInfo = createSoftMaxProgramInfo(
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inferenceHandler, inputs[0], logicalRowCount, featureCount, computeMaxProgramInfo.output.dims,
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computeScaleProgramInfo.output.dims);
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const output = inferenceHandler.run(
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{...softmaxProgramMetadata, cacheHint: attributes.cacheKey, get: () => softMaxProgramInfo},
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[inputs[0], max, scale]);
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return [output];
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};
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/**
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* Create a texture that contains the maximum value of each of the 'N' rows
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*/
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const createComputeMaxProgramInfo =
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(inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,
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outputShape: number[]): ProgramInfo => {
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const [textureWidth, textureHeight] =
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inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);
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const rank = outputShape.length;
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if (logicalRowCount < 1 || featureCount < 1) {
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throw new Error('Logical row count N and feature count D must be greater than or equal to 1');
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}
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if (outputShape.length !== 1) {
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throw new Error('Dimensionality of the output should be 1');
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}
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if (outputShape[0] !== logicalRowCount) {
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throw new Error('Shape of the output should be equal to logical row count');
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}
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const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);
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const shaderSource = `
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float process(int[${rank}] indices) {
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int logical_row_start_offset = indices[0] * ${featureCount};
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float max = getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset, ${textureWidth},
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${textureHeight} )));
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for(int i=1; i<${featureCount}; ++i)
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{
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float current = getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
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${textureWidth}, ${textureHeight})));
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if(current > max)
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max = current;
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}
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return max;
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}`;
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return {
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...softmaxComputeMaxProgramMetadata,
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output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},
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shaderSource
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};
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};
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/**
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* Create a texture that contains the normalization factor for each of the 'N' rows
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*/
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const createComputScaleProgramInfo =
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(inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,
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maxElementPerLogicalRow: readonly number[], outputShape: number[]): ProgramInfo => {
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const [textureWidth, textureHeight] =
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inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);
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const rank = outputShape.length;
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if (logicalRowCount < 1 || featureCount < 1) {
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throw new Error('Logical row count N and feature count D must be greater than or equal to 1');
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}
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if (outputShape.length !== 1) {
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throw new Error('Dimensionality of the output should be 1');
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}
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if (outputShape[0] !== logicalRowCount) {
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throw new Error('Shape of the output should be equal to logical row count');
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}
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if (maxElementPerLogicalRow.length !== 1) {
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throw new Error('Dimensionality of the intermediate results should be 1');
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}
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if (maxElementPerLogicalRow[0] !== logicalRowCount) {
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throw new Error('Shape of the intermediate results should be equal to logical row count');
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}
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const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);
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const shaderSource = `
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float process(int[${rank}] indices) {
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int logical_row_start_offset = indices[0] * ${featureCount};
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float norm_factor = 0.0;
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float max = _Max(indices);
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for(int i=0; i<${featureCount}; ++i)
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{
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norm_factor += exp(getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
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${textureWidth}, ${textureHeight}))) - max);
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}
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return norm_factor;
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}`;
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return {
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...softmaxComputeScaleProgramMetadata,
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output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},
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shaderSource
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};
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};
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const createSoftMaxProgramInfo =
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(inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,
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maxElementPerLogicalRow: readonly number[], normalizationPerLogicalRow: readonly number[]): ProgramInfo => {
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const [textureWidth, textureHeight] =
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inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);
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const rank = input.dims.length;
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if (logicalRowCount < 1 || featureCount < 1) {
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throw new Error('Logical row count N and feature count D must be greater than or equal to 1');
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}
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if (maxElementPerLogicalRow.length !== 1 || normalizationPerLogicalRow.length !== 1) {
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throw new Error('Dimensionality of the intermediate results should be 1');
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}
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if (maxElementPerLogicalRow[0] !== logicalRowCount || normalizationPerLogicalRow[0] !== logicalRowCount) {
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throw new Error('Shape of the intermediate results should be equal to logical row count');
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}
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const shaderSource = `
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float process(int[${rank}] indices) {
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// get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
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int offset = coordsToOffset(TexCoords, ${textureWidth}, ${textureHeight});
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//determine the logical row for this index
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int logical_row_index[1];
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logical_row_index[0] = offset / ${featureCount};
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float norm_factor = _Norm(logical_row_index);
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// avoid possible division by 0
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// if norm_facor is 0, all elements are zero
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// if so, return 0
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if(norm_factor == 0.0)
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return 0.0;
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return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
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}`;
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return {
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...softmaxProgramMetadata,
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output: {dims: input.dims, type: input.type, textureType: TextureType.unpacked},
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shaderSource
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};
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};
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const validateInputs = (inputs: Tensor[]): void => {
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if (!inputs || inputs.length !== 1) {
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throw new Error('Softmax requires 1 input.');
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}
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if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {
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throw new Error('Invalid input type');
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}
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};
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