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[js/webgpu] Support uniforms for instance-norm (#18929)
Co-authored-by: Guenther Schmuelling <guschmue@microsoft.com>
This commit is contained in:
Xu Xing
GitHub
parent
37ac9d391c
commit
76dfe5347c
3 changed files with 248 additions and 85 deletions
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@ -17,7 +17,7 @@ import {gather, parseGatherAttributes} from './ops/gather';
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import {gatherElements, parseGatherElementsAttributes} from './ops/gather-elements';
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import {gemm, parseGemmAttributes} from './ops/gemm';
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import {instanceNorm, parseInstanceNormAttributes} from './ops/instance-norm';
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import {layerNorm} from './ops/layer-norm';
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import {layerNorm, parseLayerNormAttributes} from './ops/layer-norm';
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import {matMul} from './ops/matmul';
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import {multiHeadAttention, parseMultiHeadAttentionAttributes} from './ops/multi-head-attentiion';
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import {pad} from './ops/pad';
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@ -83,7 +83,7 @@ export const WEBGPU_OP_RESOLVE_RULES: Map<string, OperatorImplementation> = new
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['Greater', [binaryOps.greater]],
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['GreaterOrEqual', [binaryOps.greaterOrEqual]],
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['InstanceNormalization', [instanceNorm, parseInstanceNormAttributes]],
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['LayerNormalization', [layerNorm]],
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['LayerNormalization', [layerNorm, parseLayerNormAttributes]],
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['LeakyRelu', [unaryOps.leakyRelu, unaryOps.parseAlphaAttributes]],
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['Less', [binaryOps.less]],
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['LessOrEqual', [binaryOps.lessOrEqual]],
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@ -4,58 +4,56 @@
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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 {ComputeContext, ProgramInfo, ProgramInputTensorInfoDependency, ProgramUniform} from '../types';
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import {fillVector, getMaxComponents, inputVariable, outputVariable, ShaderHelper, sumVector, tensorTypeToWsglStorageType} from './common';
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import {createTensorShapeVariables, fillVector, getMaxComponents, inputVariable, outputVariable, ShaderHelper, sumVector, tensorTypeToWsglStorageType, UniformsArrayType} from './common';
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export interface InstanceNormAttributes extends AttributeWithCacheKey {
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export interface InstanceNormAttributes {
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epsilon: number;
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format: 'NHWC'|'NCHW';
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}
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const metadata = {
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name: 'InstanceNormalization'
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};
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const createInstanceNormProgramInfo =
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(inputs: readonly TensorView[], attributes: InstanceNormAttributes): ProgramInfo => {
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const xShape = inputs[0].dims;
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const outputShape = xShape;
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const axis = 2;
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const normCount = ShapeUtil.sizeToDimension(xShape, axis);
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const normSize = ShapeUtil.sizeFromDimension(xShape, axis);
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const components = getMaxComponents(normSize);
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const normPackedSize = normSize / components;
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const C = xShape[1];
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const x = inputVariable('x', inputs[0].dataType, [xShape[0], xShape[1], normPackedSize], components);
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const scale = inputVariable('scale', inputs[1].dataType, inputs[1].dims);
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const bias = inputVariable('bias', inputs[2].dataType, inputs[2].dims);
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const output = outputVariable('output', inputs[0].dataType, [xShape[0], xShape[1], normPackedSize], components);
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const variables = [x, scale, bias, output];
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const dataType = x.type.value;
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const f32Type = components === 1 ? 'f32' : `vec${components}<f32>`;
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const workgroupSize = 64;
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const getShaderSource = (shaderHelper: ShaderHelper) => `
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const inputShape = [xShape[0], xShape[1], normPackedSize];
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const inputDependencies: ProgramInputTensorInfoDependency[] = ['rank', 'type', 'type'];
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const programUniforms: ProgramUniform[] =
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[{type: 'uint32', data: normSize}, {type: 'uint32', data: normPackedSize}];
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programUniforms.push(...createTensorShapeVariables(inputShape), ...createTensorShapeVariables(inputShape));
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const C: u32 = ${C};
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const normSize: u32 = ${normSize};
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const epsilon: f32 = ${attributes.epsilon};
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const getShaderSource = (shaderHelper: ShaderHelper) => {
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const x = inputVariable('x', inputs[0].dataType, inputShape.length, components);
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const scale = inputVariable('scale', inputs[1].dataType, inputs[1].dims);
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const bias = inputVariable('bias', inputs[2].dataType, inputs[2].dims);
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const output = outputVariable('output', inputs[0].dataType, inputShape.length, components);
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const variables = [x, scale, bias, output];
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const dataType = x.type.value;
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const f32Type = components === 1 ? 'f32' : `vec${components}<f32>`;
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const workgroupSize = 64;
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const uniforms: UniformsArrayType = [{name: 'normSize', type: 'u32'}, {name: 'normPackedSize', type: 'u32'}];
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return `
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var<workgroup> meanShared : f32;
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var<workgroup> squaredNormShared : f32;
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var<workgroup> workgroupShared : array<${f32Type}, ${workgroupSize}>;
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const workgroupSize = ${workgroupSize}u;
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${shaderHelper.declareVariables(...variables)}
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${shaderHelper.registerUniforms(uniforms).declareVariables(...variables)}
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${shaderHelper.mainStart(workgroupSize)}
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let norm = global_idx / workgroupSize;
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let batch = norm / C;
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let channel = norm % C;
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let batch = norm / uniforms.x_shape[1];
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let channel = norm % uniforms.x_shape[1];
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let localIndex = local_id.x;
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// initialize workgroup memory
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var initial = ${f32Type}(0);
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for (var h = localIndex; h < ${normPackedSize}; h += workgroupSize) {
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for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
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initial = initial + ${f32Type}(${x.get('batch', 'channel', 'h')});
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}
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workgroupShared[localIndex] = initial;
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@ -69,13 +67,13 @@ const createInstanceNormProgramInfo =
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workgroupBarrier();
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}
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if (localIndex == 0) {
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meanShared = ${sumVector('workgroupShared[0]', components)} / f32(normSize);
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meanShared = ${sumVector('workgroupShared[0]', components)} / f32(uniforms.normSize);
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}
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workgroupBarrier();
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// reinitialize workgroup memory.
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initial = ${f32Type}(0);
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for (var h = localIndex; h < ${normPackedSize}; h += workgroupSize) {
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for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
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let deviation = ${f32Type}(${x.get('batch', 'channel', 'h')}) - ${f32Type}(meanShared);
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initial = initial + deviation * deviation;
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}
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@ -94,23 +92,26 @@ const createInstanceNormProgramInfo =
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}
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workgroupBarrier();
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let invStdDev = 1 / sqrt(squaredNormShared / f32(normSize) + epsilon);
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let invStdDev = 1 / sqrt(squaredNormShared / f32(uniforms.normSize) + f32(${attributes.epsilon}));
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let channelScale = invStdDev * f32(${scale.getByOffset('channel')});
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let channelShift = f32(${bias.getByOffset('channel')}) - meanShared * channelScale;
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for (var h = localIndex; h < ${normPackedSize}; h += workgroupSize) {
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for (var h = localIndex; h < uniforms.normPackedSize; h += workgroupSize) {
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let value = ${x.get('batch', 'channel', 'h')} * ${dataType}(${f32Type}(channelScale)) + ${dataType}(${
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f32Type}(channelShift));
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f32Type}(channelShift));
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${output.set('batch', 'channel', 'h', 'value')};
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}
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}`;
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};
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return {
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...metadata,
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shaderCache: {hint: attributes.cacheKey},
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...{name: 'InstanceNormalization'},
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// TODO: use epsilon as uniform. Currently epsilon as uniform fails test_instancenorm_epsilon.
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shaderCache: {hint: `${attributes.epsilon};${components}`, inputDependencies},
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getRunData: () => ({
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outputs: [
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{dims: outputShape, dataType: inputs[0].dataType},
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],
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dispatchGroup: {x: normCount}
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dispatchGroup: {x: normCount},
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programUniforms
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}),
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getShaderSource,
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};
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@ -120,10 +121,6 @@ const computeMean =
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(context: ComputeContext, input: TensorView, scale: TensorView, bias: TensorView, n: number, h: number, c: number,
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epsilon: number) => {
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const components = getMaxComponents(c);
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const inputHelper = inputVariable('input', input.dataType, input.dims, components);
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const scaleHelper = inputVariable('scale', scale.dataType, scale.dims, components);
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const biasHelper = inputVariable('bias', bias.dataType, bias.dims, components);
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const WG = 64;
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// we will store channel scale and channel shift in [2, components] matrix
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// or in vec2 when components == 1
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@ -133,65 +130,79 @@ const computeMean =
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const unitsOfWork = n * c / components;
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const wgSize = Math.ceil(h / WG);
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const getMeanShaderSource = (shaderHelper: ShaderHelper) => `
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const H: u32 = ${h};
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const C: u32 = ${c / components};
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const imageSize: u32 = ${h * c / components};
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const meanInputDependencies: ProgramInputTensorInfoDependency[] = ['type'];
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const meanProgramUniforms: ProgramUniform[] = [
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{type: 'uint32', data: wgSize}, {type: 'uint32', data: h}, {type: 'uint32', data: Math.floor(c / components)},
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{type: 'uint32', data: Math.floor(h * c / components)}
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];
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const getMeanShaderSource = (shaderHelper: ShaderHelper) => {
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const inputHelper = inputVariable('input', input.dataType, input.dims, components);
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return `
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${shaderHelper.declareVariables(inputHelper)}
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@group(0) @binding(1) var<storage, read_write> output : array<${outputType}>;
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struct Uniforms {wg_size:u32, H:u32, C:u32, image_size:u32};
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@group(0) @binding(2) var<uniform> uniforms: Uniforms;
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${shaderHelper.mainStart(WG)}
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let currentImageNumber = global_idx / ${WG} / C;
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let currentChannelNumber = (global_idx / ${WG}) % C;
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let currentImageNumber = global_idx / ${WG} / uniforms.C;
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let currentChannelNumber = (global_idx / ${WG}) % uniforms.C;
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let wgId = global_idx % ${WG};
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let wgOffset = wgId * ${wgSize};
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if (wgOffset >= H) {
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let wgOffset = wgId * uniforms.wg_size;
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if (wgOffset >= uniforms.H) {
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return;
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}
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let wgMax = min(wgOffset + ${wgSize}, H);
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let wgMax = min(wgOffset + uniforms.wg_size, uniforms.H);
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let offset = currentImageNumber * imageSize + currentChannelNumber;
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let offset = currentImageNumber * uniforms.image_size + currentChannelNumber;
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var sum = ${fillVector('f32', components)};
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var squaredSum = ${fillVector('f32', components)};
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for (var i: u32 = wgOffset; i < wgMax; i++) {
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let value = ${sumCastType}(input[offset + i * C]);
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let value = ${sumCastType}(input[offset + i * uniforms.C]);
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sum += value;
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squaredSum += value * value;
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}
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output[global_idx] = ${setOutputValue('sum', 'squaredSum')};
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}`;
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};
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const meanValues = context.compute(
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{
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name: 'InstanceNormComputeMean',
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shaderCache: {hint: JSON.stringify({components, n, h, c})},
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shaderCache: {hint: `${components}`, inputDependencies: meanInputDependencies},
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getRunData: () => ({
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outputs: [
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{dims: [n, c, WG, 2], dataType: DataType.float},
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],
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dispatchGroup: {x: n * c / components},
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programUniforms: meanProgramUniforms
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}),
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getShaderSource: getMeanShaderSource,
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},
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{inputs: [input], outputs: [-1]})[0];
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const getShaderSource = (shaderHelper: ShaderHelper) => `
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const H: u32 = ${h};
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const C: u32 = ${c / components};
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const imageSize: u32 = ${WG * c / components};
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const epsilon: f32 = ${epsilon};
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const programUniforms: ProgramUniform[] = [
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{type: 'uint32', data: unitsOfWork}, {type: 'uint32', data: h},
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{type: 'uint32', data: Math.floor(c / components)}, {type: 'uint32', data: Math.floor(WG * c / components)}
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];
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const inputDependencies: ProgramInputTensorInfoDependency[] = ['type', 'type', 'type'];
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const getShaderSource = (shaderHelper: ShaderHelper) => {
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const scaleHelper = inputVariable('scale', scale.dataType, scale.dims, components);
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const biasHelper = inputVariable('bias', bias.dataType, bias.dims, components);
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return `
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@group(0) @binding(0) var<storage, read> input : array<${outputType}>;
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@group(0) @binding(1) var<storage, read> scale : array<${scaleHelper.type.storage}>;
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@group(0) @binding(2) var<storage, read> bias : array<${biasHelper.type.storage}>;
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@group(0) @binding(3) var<storage, read_write> output : array<${outputType}>;
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struct Uniforms {units_of_work : u32, H: u32, C : u32, image_size : u32};
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@group(0) @binding(4) var<uniform> uniforms: Uniforms;
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${shaderHelper.mainStart()}
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${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes(unitsOfWork)}
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let currentImageNumber = global_idx / C;
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let currentChannelNumber = global_idx % C;
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${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes('uniforms.units_of_work')}
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let currentImageNumber = global_idx / uniforms.C;
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let currentChannelNumber = global_idx % uniforms.C;
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let offset = currentImageNumber * imageSize;
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let offset = currentImageNumber * uniforms.image_size;
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var sum = ${fillVector('f32', components)};
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var squaredSum = ${fillVector('f32', components)};
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for (var i: u32 = 0; i < ${WG}; i++) {
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@ -199,24 +210,26 @@ const computeMean =
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sum += value[0];
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squaredSum += value[1];
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}
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sum = sum / f32(H);
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squaredSum = squaredSum / f32(H);
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let invStdDev = 1 / sqrt(squaredSum - sum * sum + epsilon);
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sum = sum / f32(uniforms.H);
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squaredSum = squaredSum / f32(uniforms.H);
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let invStdDev = 1 / sqrt(squaredSum - sum * sum + f32(${epsilon}));
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let channelScale = invStdDev * ${sumCastType}(scale[currentChannelNumber]);
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let channelShift = ${sumCastType}(bias[currentChannelNumber]) - sum * channelScale;
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output[global_idx] = ${setOutputValue('channelScale', 'channelShift')};
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}`;
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};
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return context.compute(
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{
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name: 'InstanceNormComputeChannelScaleShift',
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shaderCache: {hint: JSON.stringify({components, n, h, c, epsilon})},
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// TODO: use epsilon as uniform. Currently epsilon as uniform fails test_instancenorm_epsilon.
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shaderCache: {hint: `${components};${epsilon}`, inputDependencies},
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getRunData: () => ({
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outputs: [
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{dims: [n, c, 2], dataType: DataType.float},
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],
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dispatchGroup: {x: Math.ceil(unitsOfWork / 64 /* workgroup size */)},
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programUniforms
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}),
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getShaderSource,
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},
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@ -230,50 +243,51 @@ const createInstanceNormNHWCProgramInfo =
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const N = xShape[0];
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const C = xShape[xShape.length - 1];
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const H = ShapeUtil.sizeFromDimension(xShape, 1) / C;
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const components = getMaxComponents(C);
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const outputSize = ShapeUtil.size(outputShape) / components;
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const inputHelper = inputVariable('input', inputs[0].dataType, inputs[0].dims, components);
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const outputHelper = outputVariable('output', inputs[0].dataType, outputShape, components);
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const dataType = tensorTypeToWsglStorageType(inputs[0].dataType);
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const scaleType = components === 1 ? 'vec2f' : `mat2x${components}f`;
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const scaleCastType = components === 1 ? dataType : `vec${components}<${dataType}>`;
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const programUniforms: ProgramUniform[] =
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[{type: 'uint32', data: H}, {type: 'uint32', data: Math.floor(C / components)}];
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const inputDependencies: ProgramInputTensorInfoDependency[] = ['type', 'type'];
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// first compute mean
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const channelScaleShift = computeMean(context, inputs[0], inputs[1], inputs[2], N, H, C, attributes.epsilon);
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const getShaderSource = (shaderHelper: ShaderHelper) => {
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const dataType = tensorTypeToWsglStorageType(inputs[0].dataType);
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const scaleType = components === 1 ? 'vec2f' : `mat2x${components}f`;
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const scaleCastType = components === 1 ? dataType : `vec${components}<${dataType}>`;
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const getShaderSource = (shaderHelper: ShaderHelper) => `
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const H: u32 = ${H};
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const C: u32 = ${C / components};
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const inputHelper = inputVariable('input', inputs[0].dataType, inputs[0].dims, components);
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const outputHelper = outputVariable('output', inputs[0].dataType, outputShape, components);
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return `
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@group(0) @binding(0) var<storage, read> input : array<${inputHelper.type.storage}>;
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@group(0) @binding(1) var<storage, read> scaleInput : array<${scaleType}>;
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@group(0) @binding(2) var<storage, read_write> output : array<${outputHelper.type.storage}>;
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struct Uniforms {H: u32, C : u32};
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@group(0) @binding(3) var<uniform> uniforms: Uniforms;
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${shaderHelper.mainStart()}
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let currentImageNumber = global_idx / (C * H);
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let currentChannelNumber = global_idx % C;
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let currentImageNumber = global_idx / (uniforms.C * uniforms.H);
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let currentChannelNumber = global_idx % uniforms.C;
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let scaleOffset = currentImageNumber * C + currentChannelNumber;
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let scaleOffset = currentImageNumber * uniforms.C + currentChannelNumber;
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let scale = scaleInput[scaleOffset];
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output[global_idx] = fma(input[global_idx], ${scaleCastType}(scale[0]), ${scaleCastType}(scale[1]));
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}`;
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};
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context.compute(
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{
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name: 'InstanceNormalization',
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shaderCache: {hint: `${attributes.cacheKey}`},
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name: 'InstanceNormalizationNHWC',
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shaderCache: {hint: `${components}`, inputDependencies},
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getRunData: () => ({
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outputs: [{dims: outputShape, dataType: inputs[0].dataType}],
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dispatchGroup: {x: Math.ceil(outputSize / 64 /* workgroup size */)}
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dispatchGroup: {x: Math.ceil(outputSize / 64 /* workgroup size */)},
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programUniforms
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}),
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getShaderSource,
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},
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{inputs: [inputs[0], channelScaleShift]});
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};
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export const parseInstanceNormAttributes = (attributes: InstanceNormAttributes): InstanceNormAttributes =>
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createAttributeWithCacheKey({epsilon: attributes.epsilon, format: attributes.format});
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export const instanceNorm = (context: ComputeContext, attributes: InstanceNormAttributes): void => {
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if (attributes.format === 'NHWC') {
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createInstanceNormNHWCProgramInfo(context, context.inputs, attributes);
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@ -38,6 +38,79 @@
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}
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]
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||||
},
|
||||
{
|
||||
"name": "Simple test with NHWC, components 1",
|
||||
"operator": "InstanceNormalization",
|
||||
"inputShapeDefinitions": "rankOnly",
|
||||
"opset": { "domain": "", "version": 17 },
|
||||
"cases": [
|
||||
{
|
||||
"name": "Simple test",
|
||||
"inputs": [
|
||||
{
|
||||
"data": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 9, 8, 7, 6, 5],
|
||||
"dims": [1, 5, 3, 1],
|
||||
"type": "float32"
|
||||
},
|
||||
{
|
||||
"data": [1, 2, 3, 4, 5],
|
||||
"dims": [5],
|
||||
"type": "float32"
|
||||
},
|
||||
{
|
||||
"data": [4, 5, 6, 7, 8],
|
||||
"dims": [5],
|
||||
"type": "float32"
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"data": [
|
||||
2.775264263153076, 4, 5.224735260009766, 2.5505285263061523, 5, 7.449470520019531, 2.325794219970703, 6,
|
||||
9.674205780029297, 11.898944854736328, 7, 2.1010589599609375, 14.123676300048828, 8, 1.876321792602539
|
||||
],
|
||||
"dims": [1, 5, 3, 1],
|
||||
"type": "float32"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "Simple test with NHWC, components 2",
|
||||
"operator": "InstanceNormalization",
|
||||
"inputShapeDefinitions": "rankOnly",
|
||||
"opset": { "domain": "", "version": 17 },
|
||||
"cases": [
|
||||
{
|
||||
"name": "Simple test",
|
||||
"inputs": [
|
||||
{
|
||||
"data": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 9, 8],
|
||||
"dims": [2, 6, 1, 1],
|
||||
"type": "float32"
|
||||
},
|
||||
{
|
||||
"data": [1, 2, 3, 4, 5, 6],
|
||||
"dims": [6],
|
||||
"type": "float32"
|
||||
},
|
||||
{
|
||||
"data": [4, 5, 6, 7, 8, 9],
|
||||
"dims": [6],
|
||||
"type": "float32"
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"data": [4, 5, 6, 7, 8, 9, 4, 5, 6, 7, 8, 9],
|
||||
"dims": [2, 6, 1, 1],
|
||||
"type": "float32"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "Simple test with NCHW",
|
||||
"operator": "InstanceNormalization",
|
||||
|
|
@ -75,5 +148,81 @@
|
|||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "Simple test with NCHW, components 1",
|
||||
"operator": "InstanceNormalization",
|
||||
"opset": { "domain": "", "version": 17 },
|
||||
"cases": [
|
||||
{
|
||||
"name": "Simple test",
|
||||
"inputs": [
|
||||
{
|
||||
"data": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 9, 8, 7, 6, 5],
|
||||
"dims": [1, 5, 3, 1],
|
||||
"type": "float32"
|
||||
},
|
||||
{
|
||||
"data": [1, 2, 3, 4, 5],
|
||||
"dims": [5],
|
||||
"type": "float32"
|
||||
},
|
||||
{
|
||||
"data": [4, 5, 6, 7, 8],
|
||||
"dims": [5],
|
||||
"type": "float32"
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"data": [
|
||||
2.775264263153076, 4, 5.224735260009766, 2.5505285263061523, 5, 7.449470520019531, 2.325794219970703, 6,
|
||||
9.674205780029297, 11.898944854736328, 7, 2.1010589599609375, 14.123676300048828, 8, 1.876321792602539
|
||||
],
|
||||
"dims": [1, 5, 3, 1],
|
||||
"type": "float32"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "Simple test with NCHW, components 2",
|
||||
"operator": "InstanceNormalization",
|
||||
"opset": { "domain": "", "version": 17 },
|
||||
"cases": [
|
||||
{
|
||||
"name": "Simple test",
|
||||
"inputs": [
|
||||
{
|
||||
"data": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 9, 8, 7, 6, 5, 4, 3, 2],
|
||||
"dims": [1, 3, 6, 1],
|
||||
"type": "float32"
|
||||
},
|
||||
{
|
||||
"data": [1, 2, 3],
|
||||
"dims": [3],
|
||||
"type": "float32"
|
||||
},
|
||||
{
|
||||
"data": [4, 5, 6],
|
||||
"dims": [3],
|
||||
"type": "float32"
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"data": [
|
||||
2.5361523628234863, 3.1216912269592285, 3.70723032951355, 4.292769432067871, 4.878308296203613,
|
||||
5.4638471603393555, 1.8666191101074219, 3.9555397033691406, 6.044460296630859, 8.133380889892578,
|
||||
6.044460296630859, 3.9555397033691406, 10.3915433883667, 8.634925842285156, 6.878308296203613,
|
||||
5.121691703796387, 3.365074634552002, 1.6084575653076172
|
||||
],
|
||||
"dims": [1, 3, 6, 1],
|
||||
"type": "float32"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
|
|
|
|||
Loading…
Reference in a new issue