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[js/web] FP16 LayerNorm, InstanceNorm, SkipLayerNorm (#17630)
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### Description
This PR includes fixes for Norm operations to support FP16 and also some
optimizations to use vec2/vec4 if possible
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@dakenf
dakenf authored Oct 18, 2023
1 parent f9694c5 commit 2294710
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Showing 9 changed files with 344 additions and 190 deletions.
63 changes: 63 additions & 0 deletions js/web/lib/wasm/jsep/webgpu/ops/common.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -261,6 +261,69 @@ export const tensorTypeToWsglValueType = (type: DataType, components: 1|2|3|4 =
export const createTensorShapeVariables = (dims: readonly number[]):
ProgramUniform[] => [{type: 'uint32', data: dims}, {type: 'uint32', data: ShapeUtil.computeStrides(dims)}];

/**
* A helper function to get maximum vector size for specified data length
* @param size
*/
export const getMaxComponents = (size: number) => {
// we cannot use vec3 type since it has alignment of 16 bytes
if (size % 4 === 0) {
return 4;
} else if (size % 2 === 0) {
return 2;
}

return 1;
};

/**
* A helper function that initializes variable as a scalar or vector. e.g. f32(0) or vec4f(0,0,0,0)
* @param dataType
* @param components
* @param value
*/
export const fillVector = (dataType = 'f32', components?: number, value = '0') => {
if (!components || components === 1) {
return `${dataType}(${value})`;
}

return `vec${components}<${dataType}>(${value})`;
};

/**
* A helper function that casts value or vector to f32
* @param dataType
* @param components
* @param value
*/
export const castToF32 = (dataType: string, components: number, value: string) => {
if (dataType === 'f32') {
return value;
}
if (components === 1) {
return `f32(${value})`;
}

return `vec${components}f(${value})`;
};

/**
* A helper function that returns scalar or sums all components of a vector
* @param name
* @param components
*/
export const sumVector = (name: string, components: number) => {
if (components === 4) {
return `(${name}.x + ${name}.y + ${name}.z + ${name}.w)`;
} else if (components === 2) {
return `(${name}.x + ${name}.y)`;
} else if (components === 3) {
return `(${name}.x + ${name}.y + ${name}.z)`;
}

return name;
};

/**
* A helper function to get a IndicesHelper for a given input or output.
*
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187 changes: 139 additions & 48 deletions js/web/lib/wasm/jsep/webgpu/ops/instance-norm.ts
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Original file line number Diff line number Diff line change
@@ -1,12 +1,13 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

import {DataType} from '../../../wasm-common';
import {TensorView} from '../../tensor-view';
import {ShapeUtil} from '../../util';
import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key';
import {ComputeContext, ProgramInfo} from '../types';

import {inputVariable, outputVariable, ShaderHelper, tensorTypeToWsglStorageType} from './common';
import {fillVector, getMaxComponents, inputVariable, outputVariable, ShaderHelper, tensorTypeToWsglStorageType} from './common';

export interface InstanceNormAttributes extends AttributeWithCacheKey {
epsilon: number;
Expand Down Expand Up @@ -111,77 +112,167 @@ const createInstanceNormProgramInfo =
};
};

const computeMean =
(context: ComputeContext, input: TensorView, scale: TensorView, bias: TensorView, n: number, h: number, c: number,
epsilon: number) => {
const components = getMaxComponents(c);
const inputHelper = inputVariable('input', input.dataType, input.dims, components);
const scaleHelper = inputVariable('scale', scale.dataType, scale.dims, components);
const biasHelper = inputVariable('bias', bias.dataType, bias.dims, components);

const WG = 64;
// we will store channel scale and channel shift in [2, components] matrix
// or in vec2 when components == 1
const outputType = components === 1 ? 'vec2f' : `mat2x${components}f`;
const sumCastType = components === 1 ? 'f32' : `vec${components}f`;
const setOutputValue = (var1: string, var2: string) => `${outputType}(${var1}, ${var2})`;
const unitsOfWork = n * c / components;
const wgSize = Math.ceil(h / WG);

const getMeanShaderSource = (shaderHelper: ShaderHelper) => `
const H: u32 = ${h};
const C: u32 = ${c / components};
const imageSize: u32 = ${h * c / components};
${shaderHelper.declareVariables(inputHelper)}
@group(0) @binding(1) var<storage, read_write> output : array<${outputType}>;
${shaderHelper.mainStart(WG)}
let currentImageNumber = global_idx / ${WG} / C;
let currentChannelNumber = (global_idx / ${WG}) % C;
let wgId = global_idx % ${WG};
let wgOffset = wgId * ${wgSize};
if (wgOffset >= H) {
return;
}
let wgMax = min(wgOffset + ${wgSize}, H);
let offset = currentImageNumber * imageSize + currentChannelNumber;
var sum = ${fillVector('f32', components)};
var squaredSum = ${fillVector('f32', components)};
for (var i: u32 = wgOffset; i < wgMax; i++) {
let value = ${sumCastType}(input[offset + i * C]);
sum += value;
squaredSum += value * value;
}
output[global_idx] = ${setOutputValue('sum', 'squaredSum')};
}`;

const meanValues = context.compute(
{
name: 'InstanceNormComputeMean',
shaderCache: {hint: JSON.stringify({components, n, h, c})},
getRunData: () => ({
outputs: [
{dims: [n, c, WG, 2], dataType: DataType.float},
],
dispatchGroup: {x: n * c / components},
}),
getShaderSource: getMeanShaderSource,
},
{inputs: [input], outputs: [-1]})[0];
const getShaderSource = (shaderHelper: ShaderHelper) => `
const H: u32 = ${h};
const C: u32 = ${c / components};
const imageSize: u32 = ${WG * c / components};
const epsilon: f32 = ${epsilon};
@group(0) @binding(0) var<storage, read> input : array<${outputType}>;
@group(0) @binding(1) var<storage, read> scale : array<${scaleHelper.type.storage}>;
@group(0) @binding(2) var<storage, read> bias : array<${biasHelper.type.storage}>;
@group(0) @binding(3) var<storage, read_write> output : array<${outputType}>;
${shaderHelper.mainStart()}
${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes(unitsOfWork)}
let currentImageNumber = global_idx / C;
let currentChannelNumber = global_idx % C;
let offset = currentImageNumber * imageSize;
var sum = ${fillVector('f32', components)};
var squaredSum = ${fillVector('f32', components)};
for (var i: u32 = 0; i < ${WG}; i++) {
let value = input[offset + i + currentChannelNumber * ${WG}];
sum += value[0];
squaredSum += value[1];
}
sum = sum / f32(H);
squaredSum = squaredSum / f32(H);
let invStdDev = 1 / sqrt(squaredSum - sum * sum + epsilon);
let channelScale = invStdDev * ${sumCastType}(scale[currentChannelNumber]);
let channelShift = ${sumCastType}(bias[currentChannelNumber]) - sum * channelScale;
output[global_idx] = ${setOutputValue('channelScale', 'channelShift')};
}`;

return context.compute(
{
name: 'InstanceNormComputeChannelScaleShift',
shaderCache: {hint: JSON.stringify({components, n, h, c, epsilon})},
getRunData: () => ({
outputs: [
{dims: [n, c, 2], dataType: DataType.float},
],
dispatchGroup: {x: Math.ceil(unitsOfWork / 64 /* workgroup size */)},
}),
getShaderSource,
},
{inputs: [meanValues, scale, bias], outputs: [-1]})[0];
};

const createInstanceNormNHWCProgramInfo =
(inputs: readonly TensorView[], attributes: InstanceNormAttributes): ProgramInfo => {
(context: ComputeContext, inputs: readonly TensorView[], attributes: InstanceNormAttributes) => {
const xShape = inputs[0].dims;
const outputShape = xShape;
const outputSize = ShapeUtil.size(outputShape);
const N = xShape[0];
const C = xShape[xShape.length - 1];
const H = ShapeUtil.sizeFromDimension(xShape, 1) / C;

const components = getMaxComponents(C);
const outputSize = ShapeUtil.size(outputShape) / components;
const inputHelper = inputVariable('input', inputs[0].dataType, inputs[0].dims, components);
const outputHelper = outputVariable('output', inputs[0].dataType, outputShape, components);

const dataType = tensorTypeToWsglStorageType(inputs[0].dataType);
const scaleType = components === 1 ? 'vec2f' : `mat2x${components}f`;
const scaleCastType = components === 1 ? dataType : `vec${components}<${dataType}>`;
// first compute mean
const channelScaleShift = computeMean(context, inputs[0], inputs[1], inputs[2], N, H, C, attributes.epsilon);

const normCount = C * N;
const getShaderSource = (shaderHelper: ShaderHelper) => `
const N: u32 = ${N};
const H: u32 = ${H};
const C: u32 = ${C};
const normSizeTyped: ${dataType} = ${H};
const imageSize: u32 = ${H * C};
const epsilon: f32 = ${attributes.epsilon};
const C: u32 = ${C / components};
@group(0) @binding(0) var<storage, read> x : array<${dataType}>;
@group(0) @binding(1) var<storage, read> scale : array<${dataType}>;
@group(0) @binding(2) var<storage, read> bias : array<${dataType}>;
@group(0) @binding(3) var<storage, read_write> output : array<${dataType}>;
@group(0) @binding(0) var<storage, read> input : array<${inputHelper.type.storage}>;
@group(0) @binding(1) var<storage, read> scaleInput : array<${scaleType}>;
@group(0) @binding(2) var<storage, read_write> output : array<${outputHelper.type.storage}>;
${shaderHelper.mainStart()}
let currentImageNumber = global_idx / C;
let currentImageNumber = global_idx / (C * H);
let currentChannelNumber = global_idx % C;
// offset is channel num * N
let offset = currentImageNumber * imageSize;
if (offset >= ${outputSize}) { return; }
var mean: ${dataType} = 0;
for (var i: u32 = 0u; i < H; i++) {
mean = mean + x[offset + i * C + currentChannelNumber];
}
mean = mean / normSizeTyped;
var squaredNorm: ${dataType} = 0;
for (var i: u32 = 0u; i < H; i++) {
let deviation: f32 = x[offset + i * C + currentChannelNumber] - mean;
squaredNorm = squaredNorm + deviation * deviation;
}
let invStdDev = 1 / sqrt(squaredNorm / normSizeTyped + epsilon);
let channelScale = invStdDev * scale[currentChannelNumber];
let channelShift = bias[currentChannelNumber] - mean * channelScale;
for (var i: u32 = 0u; i < H; i++) {
let currentOffset = offset + i * C + currentChannelNumber;
output[currentOffset] = x[currentOffset] * channelScale + channelShift;
}
let scaleOffset = currentImageNumber * C + currentChannelNumber;
let scale = scaleInput[scaleOffset];
output[global_idx] = fma(input[global_idx], ${scaleCastType}(scale[0]), ${scaleCastType}(scale[1]));
}`;
return {
...metadata,
shaderCache: {hint: attributes.cacheKey},
getRunData: () => ({
outputs: [
{dims: outputShape, dataType: inputs[0].dataType},
],
dispatchGroup: {x: Math.ceil(normCount / 64 /* workgroup size */)}
}),
getShaderSource,
};
context.compute(
{
name: 'InstanceNormalization',
shaderCache: {hint: `${attributes.cacheKey}`},
getRunData: () => ({
outputs: [{dims: outputShape, dataType: inputs[0].dataType}],
dispatchGroup: {x: Math.ceil(outputSize / 64 /* workgroup size */)}
}),
getShaderSource,
},
{inputs: [inputs[0], channelScaleShift]});
};

export const parseInstanceNormAttributes = (attributes: InstanceNormAttributes): InstanceNormAttributes =>
createAttributeWithCacheKey({epsilon: attributes.epsilon, format: attributes.format});

export const instanceNorm = (context: ComputeContext, attributes: InstanceNormAttributes): void => {
if (attributes.format === 'NHWC') {
context.compute(createInstanceNormNHWCProgramInfo(context.inputs, attributes));
createInstanceNormNHWCProgramInfo(context, context.inputs, attributes);
} else {
context.compute(createInstanceNormProgramInfo(context.inputs, attributes));
}
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