mirror of
https://github.com/saymrwulf/onnxruntime.git
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0ca84549ab
### Description <!-- Describe your changes. --> Added uniforms to Reduce op ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Improve perforamnce.
83 lines
3.2 KiB
TypeScript
83 lines
3.2 KiB
TypeScript
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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// TODO: this is the same naive implementation we use for reduce that has
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// performance limitations when the reduced axis is long. Need to add
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// a optimized codepath for this.
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import {DataType} from '../../../wasm-common';
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import {TensorView} from '../../tensor-view';
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import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key';
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import {ComputeContext} from '../types';
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import {createReduceProgramInfo, ReduceOp} from './reduce';
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const validateInputs = (inputs: readonly TensorView[]): void => {
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if (!inputs || inputs.length === 0 || inputs.length > 2) {
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throw new Error('ArgMinMaxOp op requires 1 or 2 inputs.');
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}
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if (inputs[0].dataType !== DataType.float) {
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throw new Error('Invalid input type.');
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}
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};
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export interface ArgMinMaxAttributes extends AttributeWithCacheKey {
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keepDims: boolean;
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axis: number;
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selectLastIndex: number;
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}
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export const argMin = (context: ComputeContext, attributes: ArgMinMaxAttributes): void => {
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validateInputs(context.inputs);
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const argMinMaxOp: ReduceOp = (input, output, axes) => {
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const idxZero = [];
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for (let k = 0; k < input.rank; k++) {
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if (axes.indexOf(k) >= 0 || axes.length === 0) {
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idxZero.push(`input_indices[${k}] = 0;`); // first element
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}
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}
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return [
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`${idxZero.join('\n')}`, `var value = ${input.getByIndices('input_indices')};\nvar best_index : i32 = 0;`,
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`if (${input.getByIndices('input_indices')} ${attributes.selectLastIndex > 0 ? '<=' : '<'} value) {
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value = ${input.getByIndices('input_indices')};
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best_index = i32(last_index);
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}`,
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'', output.setByOffset('global_idx', 'best_index')
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];
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};
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context.compute(
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createReduceProgramInfo(
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'ArgMin', {hint: attributes.cacheKey, inputDependencies: ['rank']}, [context.inputs[0]], argMinMaxOp,
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[attributes.axis], DataType.int64, attributes.keepDims),
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{inputs: [0]});
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};
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export const argMax = (context: ComputeContext, attributes: ArgMinMaxAttributes): void => {
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validateInputs(context.inputs);
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const argMinMaxOp: ReduceOp = (input, output, axes) => {
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const idxZero = [];
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for (let k = 0; k < input.rank; k++) {
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if (axes.indexOf(k) >= 0 || axes.length === 0) {
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idxZero.push(`input_indices[${k}] = 0;`); // first element
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}
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}
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return [
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`${idxZero.join('\n')}`, `var value = ${input.getByIndices('input_indices')};\nvar best_index : i32 = 0;`,
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`if (${input.getByIndices('input_indices')} ${attributes.selectLastIndex > 0 ? '>=' : '>'} value) {
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value = ${input.getByIndices('input_indices')};
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best_index = i32(last_index);
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}`,
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'', output.setByOffset('global_idx', 'best_index')
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];
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};
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context.compute(
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createReduceProgramInfo(
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'argMax', {hint: attributes.cacheKey, inputDependencies: ['rank']}, [context.inputs[0]], argMinMaxOp,
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[attributes.axis], DataType.int64, attributes.keepDims),
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{inputs: [0]});
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};
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export const parseArgMinMaxAttributes = (attributes: Record<string, unknown>): ArgMinMaxAttributes =>
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createAttributeWithCacheKey(attributes as Omit<ArgMinMaxAttributes, keyof AttributeWithCacheKey>);
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