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### Description <!-- Describe your changes. --> Optimize conv1d to go to the conv2d path to utilize the conv2d's optimization path. See whisper-tiny-encoder model becomes 158.66 ms from 532.28 ms. Conv goes to Conv2DMatMul(8 ms) instead of GroupedConv(382 ms). Old profiling result: Kernel | Time (ms) | Percentage (%) -- | -- | -- Conv\|GroupedConv | 382.99 | 71.95 MatMul | 126.16 | 23.70 Softmax | 7.01 | 1.32 Transpose | 4.59 | 0.86 Add | 4.39 | 0.82 Mul | 2.36 | 0.44 Div | 1.44 | 0.27 ReduceMean\|ReduceMeanShared | 1.25 | 0.23 Erf | 0.85 | 0.16 Sub | 0.72 | 0.14 Pow | 0.46 | 0.09 Sqrt | 0.07 | 0.01 Sum | 532.28 | New profiling result with this PR: Kernel | Time (ms) | Percentage (%) -- | -- | -- MatMul | 127.07 | 80.09 Conv\|Conv2DMatMul | 8.00 | 5.04 Softmax | 6.95 | 4.38 Transpose | 4.65 | 2.93 Add | 4.26 | 2.68 Mul | 2.56 | 1.61 Div | 1.51 | 0.95 ReduceMean\|ReduceMeanShared | 1.31 | 0.83 Erf | 0.85 | 0.54 Sub | 0.79 | 0.50 Pow | 0.46 | 0.29 Conv\|Transpose | 0.26 | 0.17 Sqrt | 0.00 | 0.00 Sum | 158.66 | --------- Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
107 lines
4.4 KiB
TypeScript
107 lines
4.4 KiB
TypeScript
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import { DataType } from '../../../wasm-common';
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import { TensorView } from '../../tensor-view';
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import { ShapeUtil } from '../../util';
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import { AttributeWithCacheKey, createAttributeWithCacheKey } from '../attribute-with-cache-key';
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import { ComputeContext, ProgramInfo } from '../types';
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import { createTensorShapeVariables, IndicesHelper, inputVariable, outputVariable, ShaderHelper } from './common';
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export interface TransposeAttributes extends AttributeWithCacheKey {
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readonly perm: number[];
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}
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const validateInputs = (inputs: readonly TensorView[]): void => {
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if (!inputs || inputs.length !== 1) {
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throw new Error('Transpose requires 1 input.');
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}
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};
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const getAdjustedPerm = (inputRank: number, perm: number[]): number[] =>
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perm && perm.length !== inputRank ? [...new Array(inputRank).keys()].reverse() : perm;
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const getOutputShape = (inputShape: readonly number[], perm: number[]): readonly number[] =>
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ShapeUtil.sortBasedOnPerm(inputShape, getAdjustedPerm(inputShape.length, perm));
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const permFunctionBody = (perm: number[], rank: number, input: IndicesHelper, output: IndicesHelper): string => {
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const reverseFunc = [];
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reverseFunc.push(`fn perm(i: ${output.type.indices}) -> ${input.type.indices} {
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var a: ${input.type.indices};`);
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for (let i = 0; i < rank; ++i) {
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reverseFunc.push(input.indicesSet('a', perm[i], `i[${i}]`));
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}
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reverseFunc.push('return a;}');
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return reverseFunc.join('\n');
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};
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export const createTransposeProgramInfo = (inputTensor: TensorView, permAttr: number[]): ProgramInfo => {
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const inputDataType = inputTensor.dataType;
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const inputRank = inputTensor.dims.length;
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const perm = getAdjustedPerm(inputRank, permAttr);
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const outputShape = getOutputShape(inputTensor.dims, perm);
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const output = outputVariable('output', inputDataType, outputShape.length);
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const input = inputVariable('a', inputDataType, inputRank);
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let getShaderSource;
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if (perm.length === 2 && perm[0] === 1 && perm[1] === 0) {
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const wgslType = output.type.value;
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const workgroupSize: [number, number, number] = [16, 16, 1];
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getShaderSource = (shaderHelper: ShaderHelper) => `
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${shaderHelper.registerUniform('output_size', 'u32').declareVariables(input, output)}
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var<workgroup> tile : array<array<${wgslType}, ${workgroupSize[0] + 1}>, ${workgroupSize[0]}>;
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${shaderHelper.mainStart(workgroupSize)}
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var x = workgroup_id.x * ${workgroupSize[0]}u + local_id.x;
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var y = workgroup_id.y * ${workgroupSize[0]}u + local_id.y;
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let width = uniforms.output_shape[0];
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let height = uniforms.output_shape[1];
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if (x < width && y < height) {
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tile[local_id.y][local_id.x] = ${input.getByOffset('y * width + x')};
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}
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workgroupBarrier();
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x = workgroup_id.y * ${workgroupSize[0]}u + local_id.x;
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y = workgroup_id.x * ${workgroupSize[0]}u + local_id.y;
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if (x < height && y < width) {
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${output.setByOffset('y * height + x', 'tile[local_id.x][local_id.y]')}
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}
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}`;
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} else {
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getShaderSource = (shaderHelper: ShaderHelper) => `
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${shaderHelper.registerUniform('output_size', 'u32').declareVariables(input, output)}
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${permFunctionBody(perm, inputRank, input, output)}
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${shaderHelper.mainStart()}
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${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes('uniforms.output_size')}
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let indices = ${output.offsetToIndices('global_idx')};
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let aIndices = perm(indices);
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${output.setByOffset('global_idx', input.getByIndices('aIndices'))}
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}`;
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}
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return {
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name: 'Transpose',
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shaderCache: { hint: `${permAttr}`, inputDependencies: ['rank'] },
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getRunData: () => {
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const outputSize = ShapeUtil.size(outputShape);
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return {
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outputs: [{ dims: outputShape, dataType: inputTensor.dataType }],
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dispatchGroup: { x: Math.ceil(outputSize / 64 /* workgroup size */) },
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programUniforms: [
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{ type: DataType.uint32, data: outputSize },
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...createTensorShapeVariables(inputTensor.dims, outputShape),
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],
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};
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},
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getShaderSource,
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};
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};
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export const transpose = (context: ComputeContext, attributes: TransposeAttributes): void => {
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validateInputs(context.inputs);
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context.compute(createTransposeProgramInfo(context.inputs[0], attributes.perm));
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};
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export const parseTransposeAttributes = (attributes: Record<string, unknown>): TransposeAttributes =>
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createAttributeWithCacheKey({ perm: attributes.perm as number[] });
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