onnxruntime-web
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A Javascript library for running ONNX models on browsers
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text/typescript
// 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, ProgramUniform} from '../types';
import {createTensorShapeVariables, getMaxComponents, inputVariable, outputVariable, ShaderHelper, tensorTypeToWsglStorageType, UniformsArrayType} from './common';
// TODO support quantization bits not equal to 4
export interface MatMulNBitsAttributes extends AttributeWithCacheKey {
k: number;
n: number;
accuracyLevel: number;
bits: number;
blockSize: number;
}
const validateInputs = (inputs: readonly TensorView[], attributes: MatMulNBitsAttributes): void => {
if (inputs.length < 3 || inputs.length > 4) {
throw new Error('MatMulNBits requires 3 or 4 inputs');
}
const a = inputs[0];
const aRank = a.dims.length;
if (a.dims[aRank - 1] !== attributes.k) {
throw new Error('The last dim of input shape does not match the k value');
}
const nBlocksPerCol = Math.floor((attributes.k + attributes.blockSize - 1) / attributes.blockSize);
const blobSize = attributes.blockSize / 8 * attributes.bits;
const b = inputs[1];
if (!ShapeUtil.areEqual(b.dims, [attributes.n, nBlocksPerCol, blobSize])) {
throw new Error('The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize');
}
const scales = inputs[2];
const scalesShape = scales.dims;
if (ShapeUtil.size(scalesShape) !== attributes.n * nBlocksPerCol) {
throw new Error('scales input size error.');
}
if (inputs.length === 4) {
const zeroPoints = inputs[3];
const zeroPointsShape = zeroPoints.dims;
const expectedZeroPointsSize =
attributes.bits > 4 ? (attributes.n * nBlocksPerCol) : attributes.n * Math.floor((nBlocksPerCol + 1) / 2);
if (ShapeUtil.size(zeroPointsShape) !== expectedZeroPointsSize) {
throw new Error('zeroPoints input size error.');
}
}
};
export const createMatMulNBitsProgramInfo =
(inputs: readonly TensorView[], attributes: MatMulNBitsAttributes): ProgramInfo => {
const inputShape = inputs[0].dims;
const aRank = inputShape.length;
const outputShape = inputShape.slice(0, aRank - 1).concat(attributes.n);
const m = inputShape[aRank - 2];
const blobSize = attributes.blockSize / 8 * attributes.bits;
const blobSizeInWords = blobSize / 4;
const outputNumber = getMaxComponents(m);
const components = getMaxComponents(attributes.n);
const aComponents = getMaxComponents(attributes.k);
const bComponents = getMaxComponents(blobSizeInWords);
const outputSize = ShapeUtil.size(outputShape) / components / outputNumber;
const programUniforms: ProgramUniform[] = [
{type: DataType.uint32, data: outputSize}, {type: DataType.uint32, data: attributes.k},
{type: DataType.uint32, data: attributes.n}, {type: DataType.uint32, data: attributes.accuracyLevel},
{type: DataType.uint32, data: attributes.bits}, {type: DataType.uint32, data: attributes.blockSize}
];
const aShape = inputShape.slice();
aShape.splice(-1, 1, attributes.k / aComponents);
const bShape = ShapeUtil.convertShape(inputs[1].dims).slice();
bShape.splice(-1, 1, blobSizeInWords / bComponents);
programUniforms.push(...createTensorShapeVariables(aShape));
programUniforms.push(...createTensorShapeVariables(bShape));
programUniforms.push(...createTensorShapeVariables(inputs[2].dims));
if (inputs.length === 4) {
programUniforms.push(...createTensorShapeVariables(ShapeUtil.convertShape(inputs[3].dims)));
}
const oShape = outputShape.slice();
oShape.splice(-1, 1, attributes.n / components);
programUniforms.push(...createTensorShapeVariables(oShape));
const getShaderSource = (shaderHelper: ShaderHelper) => {
const a = inputVariable('a', inputs[0].dataType, aShape.length, aComponents);
const b = inputVariable('b', DataType.uint32, bShape.length, bComponents);
const scales = inputVariable('scales', inputs[2].dataType, inputs[2].dims.length);
const inputVariables = [a, b, scales];
const zeroPoints =
inputs.length === 4 ? inputVariable('zero_points', DataType.uint32, inputs[3].dims.length) : undefined;
if (zeroPoints) {
inputVariables.push(zeroPoints);
}
const output = outputVariable('output', inputs[0].dataType, outputShape.length, components);
const uniforms: UniformsArrayType = [
{name: 'output_size', type: 'u32'}, {name: 'K', type: 'u32'}, {name: 'N', type: 'u32'},
{name: 'accuracy_level', type: 'u32'}, {name: 'bits', type: 'u32'}, {name: 'block_size', type: 'u32'}
];
const nBlocksPerCol = Math.floor((attributes.k + attributes.blockSize - 1) / attributes.blockSize);
const dataType = tensorTypeToWsglStorageType(inputs[0].dataType);
const qDqDataType = (() => {
switch (aComponents) {
case 1:
return `array<${dataType}, 8>`;
case 2:
return `mat4x2<${dataType}>`;
case 4:
return `mat2x4<${dataType}>`;
default:
throw new Error(`${aComponents}-component is not supported.`);
}
})();
const dequantizeImpl = `
fn dequantize(quantized: ${qDqDataType}, zero_point: ${dataType}, scale: ${dataType}) -> ${qDqDataType} {
${(() => {
if (aComponents === 1) {
return `var dequantized = ${qDqDataType}(${
Array.from({length: 8}, (_, i) => `(quantized[${i}] - zero_point) * scale`).join(', ')});
return dequantized;`;
} else {
return `var zero_points: ${qDqDataType} = ${qDqDataType}(${Array(8).fill('zero_point').join(',')});
return (quantized - zero_points) * scale;`;
}
})()}
}`;
const ortUnpack8x4snormImpl = `
fn ortUnpack8x4snorm(value: u32) -> ${qDqDataType} {
var quantized: ${qDqDataType};
var offset: u32 = 0;
let count: u32 = 4;
for (var i: u32 = 0; i < 8u; i++) {
var result = ${dataType}(extractBits(value, offset, count));
${(() => {
switch (aComponents) {
case 1:
return 'quantized[i] = result;';
case 2:
return 'quantized[i / 2][i % 2] = result;';
case 4:
return 'quantized[i / 4][i % 4] = result;';
default:
throw new Error(`${aComponents}-component is not supported.`);
}
})()}
offset += count;
}
return quantized;
}`;
const updateZeroPointIndex = zeroPoints ? `
zero_point_offset += 4;
if (zero_point_offset == 32) {
zero_point_offset = 0;
zero_point_index++;
zero_point_word = ${zeroPoints.getByOffset('zero_point_index')};
}` :
'';
return `
${dequantizeImpl};
${ortUnpack8x4snormImpl};
${shaderHelper.registerUniforms(uniforms).declareVariables(...inputVariables, output)}
${shaderHelper.mainStart()}
${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes('uniforms.output_size')}
var output_values: array<${output.type.value}, ${outputNumber}>;
var output_indices = ${output.offsetToIndices('global_idx')};
var n = ${output.indicesGet('output_indices', aRank - 1)};
var m = ${output.indicesGet('output_indices', aRank - 2)};
var a_indices: ${a.type.indices} = output_indices;
// Two zero points are packed into one byte because uniforms.bits <= 4.
// zero_point_offset is either 0 or 4. It is bit offset within one byte.
// TODO support zero_point_offset for bits > 4
${
zeroPoints ? `
var zero_point_index: u32 = n * ${components} * ((${nBlocksPerCol} + 1) / 2) / 4;
var zero_point_word: u32 = ${zeroPoints.getByOffset('zero_point_index')};
var zero_point_offset: u32 = 0;` :
''}
var scale_index = n * ${nBlocksPerCol * components};
var b_indices: ${b.type.indices};
for (var c: u32 = 0; c < ${components}; c++) {
${b.indicesSet('b_indices', '0', `n * ${components} + c`)};
var block_offset: u32 = 0;
for (var block: u32 = 0; block < ${nBlocksPerCol}; block++) {
// The scale and zero points are computed per block.
let scale = ${scales.getByOffset('scale_index')};
// The default zero point is 8 for unsigned 4-bit quantization.
let zero_point = ${dataType}(${zeroPoints ? 'extractBits(zero_point_word, zero_point_offset, 4)' : 8.0});
${b.indicesSet('b_indices', '1', 'block')};
var word_offset: u32 = block_offset;
for (var word: u32 = 0; word < ${blobSizeInWords}; word += ${bComponents}) {
${b.indicesSet('b_indices', '2', 'word')};
let b_data = ${b.getByIndices('b_indices')};
for (var i: u32 = 0; i < ${bComponents}; i++) {
let b_value = ${bComponents === 1 ? 'b_data' : 'b_data[word + i]'};
let b_quantized_values: ${qDqDataType} = ortUnpack8x4snorm(b_value);
let b_dequantized_values = dequantize(b_quantized_values, zero_point, scale);
// Number of B elements per 32-bit word is 32/bits = 32/4 = 8
var offset: u32 = word_offset;
for (var j: u32 = 0; j < 8/${aComponents}; j++) {
${a.indicesSet('a_indices', aRank - 1, `offset/${aComponents}`)};
for (var k: u32 = 0; k < ${outputNumber}u; k++) {
${a.indicesSet('a_indices', aRank - 2, `m * ${outputNumber} + k`)};
let a_data = ${a.getByIndices('a_indices')};
output_values[k]${components > 1 ? '[c]' : ''} += ${
aComponents === 1 ? 'a_data * b_dequantized_values[j]' : 'dot(a_data, b_dequantized_values[j])'};
}
offset += ${aComponents};
}
word_offset += 8;
}
}
scale_index++;
${updateZeroPointIndex}
block_offset += uniforms.block_size;
}
// Drop the trailing 4 bits if the zero_poit_offset is not a byte boundary to align with the next byte.
${
zeroPoints ? `if (zero_point_offset % 8 > 0) {
${updateZeroPointIndex}
}` :
''}
}
for (var k: u32 = 0u; k < ${outputNumber}u; k++) {
${output.indicesSet('output_indices', aRank - 2, `${outputNumber + ' * m + k'}`)};
${output.setByIndices('output_indices', 'output_values[k]')}
}
}`;
};
return {
name: 'MatMulNBits',
shaderCache:
{hint: `${attributes.cacheKey};${inputs.length}`, inputDependencies: Array(inputs.length).fill('rank')},
getRunData: () => ({
outputs: [{dims: outputShape, dataType: inputs[0].dataType}],
dispatchGroup: {x: Math.ceil(outputSize / 64 /* workgroup size */)},
programUniforms
}),
getShaderSource
};
};
export const matMulNBits = (context: ComputeContext, attributes: MatMulNBitsAttributes): void => {
validateInputs(context.inputs, attributes);
context.compute(createMatMulNBitsProgramInfo(context.inputs, attributes));
};
export const parseMatMulNBitsAttributes = (attributes: Record<string, unknown>): MatMulNBitsAttributes =>
createAttributeWithCacheKey(attributes as Omit<MatMulNBitsAttributes, keyof AttributeWithCacheKey>);