// Copyright (c) Microsoft Corporation. All rights reserved. // Licensed under the MIT License. import {DataType} from '../../../wasm-common'; import {TensorView} from '../../tensor'; import {ShapeUtil} from '../../util'; import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key'; import {ComputeContext, GpuDataType, ProgramInfo, ProgramInfoLoader, ProgramMetadata} from '../types'; import {IndicesHelper, inputVariable, outputVariable, ShaderHelper} from './common'; const validateInputs = (inputs: readonly TensorView[]): void => { if (!inputs || inputs.length === 0 || inputs.length > 2) { throw new Error('Reduce op requires 1 or 2 inputs.'); } if (inputs.length === 2 && inputs[1].dims.length !== 1) { throw new Error('Invalid axes input dims.'); } if (inputs[0].dataType !== DataType.float) { throw new Error('Invalid input type.'); } }; export interface ReduceAttributes extends AttributeWithCacheKey { keepDims: boolean; noopWithEmptyAxes: boolean; axes: number[]; } export type ReduceOp = (input: IndicesHelper, output: IndicesHelper, axes: readonly number[]) => [string, string, string, string, ...string[]]; const noOp: ReduceOp = (input) => ['', '', `var value = ${input.getByOffset('inputOffset')};`, '']; export const createReduceProgramInfo = (metadata: ProgramMetadata, inputs: readonly TensorView[], reduceOp: ReduceOp, axesInput: number[], outputDataType: DataType, keepDims = false, noopWithEmptyAxes = false): ProgramInfo => { const outputShape: number[] = []; const inputShape = inputs[0].dims; const axes = ShapeUtil.normalizeAxes(axesInput, inputs[0].dims.length); const reduceOnAllAxes = !noopWithEmptyAxes && axes.length === 0; inputShape.forEach((d, i) => { if (reduceOnAllAxes || axes.indexOf(i) >= 0) { if (keepDims) { outputShape.push(1); } // else { // skip this axis} } else { outputShape.push(d); } }); const idxCopy: string[] = []; // copy output indexes to input indexes const input = inputVariable('_A', inputs[0].dataType, inputShape); const output = outputVariable('output', outputDataType, outputShape); const ops = reduceOp(input, output, axes); const inputOffsetAssignment = `inputOffset = ${input.indicesToOffset('inputIndices')};`; const initinputOffsetLet = `let ${inputOffsetAssignment};`; const initinputOffsetVar = `var ${inputOffsetAssignment};`; const initinputOffset = (ops[1] === '') ? '' : initinputOffsetVar; let reduceOps = ((ops[1] === '') ? initinputOffsetLet : inputOffsetAssignment) + '\n' + ops[2]; for (let k = 0, l = 0; k < inputs[0].dims.length; k++) { // if this axis is reduced if (reduceOnAllAxes || axes.indexOf(k) >= 0) { if (keepDims) { l++; } // loop over the d-th axis reduceOps = `for(var j${k}: u32 = 0; j${k} < ${inputs[0].dims[k]}; j${k}++) { ${ops[2].includes('lastIndex') ? `let lastIndex = j${k};` : ''} ${input.indicesSet('inputIndices', k, `j${k}`)} ${reduceOps} }`; } else { idxCopy.push(`${input.indicesSet('inputIndices', k, output.indicesGet('outputIndices', l))};`); l++; } } const outputSize = ShapeUtil.size(outputShape); const getShaderSource = (shaderHelper: ShaderHelper) => ` ${shaderHelper.declareVariables(input, output)} ${shaderHelper.mainStart()} ${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes(outputSize)} var inputIndices: ${input.type.indices}; let outputIndices = ${output.offsetToIndices('global_idx')}; ${idxCopy.join('\n')} ${ops[0]} // init ops for reduce max/min ${initinputOffset} ${ops[1]} ${reduceOps} ${ops[3]} ${ops.length === 4 ? output.setByOffset('global_idx', 'value') : ops.slice(4).join('\n')} }`; return { ...metadata, getShaderSource, outputs: [{dims: outputShape, dataType: outputDataType, gpuDataType: GpuDataType.default}], dispatchGroup: () => ({x: Math.ceil(outputSize / 64 /* workgroup size */)}) }; }; const createReduceAttributesFromInputs = (inputs: readonly TensorView[], attributes: ReduceAttributes): ReduceAttributes => { const axes: number[] = []; if (inputs[1].dims[0] > 0) { inputs[1].getBigInt64Array().forEach(v => axes.push(Number(v))); } return createAttributeWithCacheKey( {axes, keepDims: attributes.keepDims, noopWithEmptyAxes: attributes.noopWithEmptyAxes}); }; const createReduceProgramInfoLoader = (inputs: readonly TensorView[], name: string, attributes: ReduceAttributes, reduceOp: ReduceOp): ProgramInfoLoader => { const updatedAttributes: ReduceAttributes = inputs.length === 1 ? attributes : createReduceAttributesFromInputs(inputs, attributes); const metadata: ProgramMetadata = { name, inputTypes: [GpuDataType.default], cacheHint: updatedAttributes.cacheKey + '_' + inputs[0].dims.map(d => d.toString()).join(',') }; return { ...metadata, get: () => createReduceProgramInfo( metadata, [inputs[0]], updatedAttributes.noopWithEmptyAxes && updatedAttributes.axes.length === 0 ? noOp : reduceOp, updatedAttributes.axes, inputs[0].dataType, updatedAttributes.keepDims, updatedAttributes.noopWithEmptyAxes) }; }; export const reduceLogSum = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, output) => [`var value = ${output.type.storage}(0);`, '', `value += ${input.getByOffset('inputOffset')};`, 'value = log(value);', ]; context.compute(createReduceProgramInfoLoader(context.inputs, 'ReduceLogSum', attributes, reduceOp), {inputs: [0]}); }; export const reduceL1 = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, output) => [`var value = ${output.type.storage}(0);`, '', `value += abs(${input.getByOffset('inputOffset')});`, '', ]; context.compute(createReduceProgramInfoLoader(context.inputs, 'ReduceL1', attributes, reduceOp), {inputs: [0]}); }; export const reduceL2 = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, output) => [`var t = f32(0); var value = ${output.type.storage}(0);`, '', `t = ${input.getByOffset('inputOffset')}; value += (t * t);`, 'value = sqrt(value);', ]; context.compute(createReduceProgramInfoLoader(context.inputs, 'ReduceL2', attributes, reduceOp), {inputs: [0]}); }; export const reduceLogSumExp = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, output) => [`var value = ${output.type.storage}(0);`, '', `value += exp(${input.getByOffset('inputOffset')});`, 'value = log(value);', ]; context.compute( createReduceProgramInfoLoader(context.inputs, 'ReduceLogSumExp', attributes, reduceOp), {inputs: [0]}); }; export const reduceMax = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, _output, axes) => { const idxZero = []; for (let k = 0; k < input.shape.length; k++) { if (axes.indexOf(k) >= 0 || axes.length === 0) { idxZero.push(input.indicesSet('inputIndices', k, 0)); } } return [ `${idxZero.join('\n')}`, `var value = ${input.getByOffset('inputOffset')};`, `value = max(value, ${input.getByOffset('inputOffset')});`, '', ]; }; context.compute(createReduceProgramInfoLoader(context.inputs, 'ReduceMax', attributes, reduceOp), {inputs: [0]}); }; export const reduceMean = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, output, axes) => { let size = 1.0; for (let k = 0; k < input.shape.length; k++) { if (axes.indexOf(k) >= 0 || axes.length === 0) { size *= input.shape[k]; } } return [ `var value = ${output.type.storage}(0);`, '', `value += ${input.getByOffset('inputOffset')};`, `value = value / ${size}.;`, ]; }; context.compute(createReduceProgramInfoLoader(context.inputs, 'ReduceMean', attributes, reduceOp), {inputs: [0]}); }; export const reduceMin = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, _output, axes) => { const idxZero = []; for (let k = 0; k < input.shape.length; k++) { if (axes.indexOf(k) >= 0 || axes.length === 0) { idxZero.push(`inputIndices[${k}] = 0;`); // first element } } return [ `${idxZero.join('\n')}`, `var value = ${input.getByOffset('inputOffset')};`, `value = min(value, ${input.getByOffset('inputOffset')});`, '', ]; }; context.compute(createReduceProgramInfoLoader(context.inputs, 'ReduceMin', attributes, reduceOp), {inputs: [0]}); }; export const reduceProd = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, output) => [`var value = ${output.type.storage}(1);`, '', `value *= ${input.getByOffset('inputOffset')};`, '', ]; context.compute(createReduceProgramInfoLoader(context.inputs, 'ReduceProd', attributes, reduceOp), {inputs: [0]}); }; export const reduceSum = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, output) => [`var value = ${output.type.storage}(0);`, '', `value += ${input.getByOffset('inputOffset')};`, '', ]; context.compute(createReduceProgramInfoLoader(context.inputs, 'ReduceSum', attributes, reduceOp), {inputs: [0]}); }; export const reduceSumSquare = (context: ComputeContext, attributes: ReduceAttributes): void => { validateInputs(context.inputs); const reduceOp: ReduceOp = (input, output) => [`var t = f32(0); var value = ${output.type.storage}(0);`, '', `t = ${input.getByOffset('inputOffset')}; value += t * t;`, '', ]; context.compute( createReduceProgramInfoLoader(context.inputs, 'ReduceSumSquare', attributes, reduceOp), {inputs: [0]}); }; export const parseReduceAttributes = (attributes: Record): ReduceAttributes => createAttributeWithCacheKey(attributes as Omit);