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completed lstm #71

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merged 13 commits into from
Apr 16, 2024
Merged

completed lstm #71

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abfceb9
complete lstm_cell
mei1127 Feb 28, 2024
9a7e0a4
revised lstm_cell.js and lstm_cell_test.js
mei1127 Feb 28, 2024
24d6d03
completed lstm
mei1127 Mar 1, 2024
8d6e71e
revised lstm.js lstm_cell.js and validate-input.js
mei1127 Mar 1, 2024
95c0ab1
revised validata-input.js to ensure length<max-len
mei1127 Mar 1, 2024
6995143
revised some files related to squeeze
mei1127 Mar 4, 2024
bda1651
revised reduce.js
mei1127 Mar 4, 2024
c451f91
revised arg_max_min.js
mei1127 Mar 4, 2024
cf52d51
Merge branch 'main' into add_lstm
mei1127 Mar 4, 2024
190fe60
revised reshape.js
mei1127 Mar 4, 2024
76746d9
Merge branch 'add_lstm' of https://github.com/mei1127/webnn-baseline …
mei1127 Mar 4, 2024
d8ebc93
revised reshape.js
mei1127 Mar 4, 2024
5b098bc
revised reshape.js
mei1127 Mar 4, 2024
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166 changes: 164 additions & 2 deletions src/lib/validate-input.js
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Original file line number Diff line number Diff line change
Expand Up @@ -133,6 +133,168 @@ export function validateGemmParams(a, b) {
}
}

export function validateLstmCellParams(input, weight, recurrentWeight,
hiddenState, cellState, hiddenSize,
{bias, recurrentBias, peepholeWeight, layout = 'iofg'} = {}) {
if (!Number.isInteger(hiddenSize) || hiddenSize <= 0) {
throw new Error(`The hiddenSize ${hiddenSize} is invalid.`);
}
if (input.rank !== 2) {
throw new Error(`The input (rank ${input.rank}) is not a 2-D tensor.`);
}
const batchSize = input.shape[0];
const inputSize = input.shape[1];
if (weight.rank !== 2) {
throw new Error(`The weight (rank ${weight.rank}) is not a 2-D tensor.`);
}
if (weight.shape[0] !== 4 * hiddenSize || weight.shape[1] !== inputSize) {
throw new Error(`The shape of weight [${weight.shape[0]}, ${weight.shape[1]}] is invalid.`);
}
if (recurrentWeight.rank !== 2) {
throw new Error(`The recurrentWeight (rank ${recurrentWeight.rank}) is not a 2-D tensor.`);
}
if (recurrentWeight.shape[0] !== 4 * hiddenSize || recurrentWeight.shape[1] !== hiddenSize) {
throw new Error(`The shape of recurrentWeight ` +
`[${recurrentWeight.shape[0]}, ${recurrentWeight.shape[1]}] is invalid.`);
}
if (hiddenState.rank !== 2) {
throw new Error(`The hiddenState (rank ${hiddenState.rank}) is not a 2-D tensor.`);
}
if (hiddenState.shape[0] !== batchSize || hiddenState.shape[1] !== hiddenSize) {
throw new Error(`The shape of hiddenState
[${hiddenState.shape[0]}, ${hiddenState.shape[1]}] is invalid.`);
}
if (cellState.rank !== 2) {
throw new Error(`The cellState (rank ${cellState.rank}) is not a 2-D tensor.`);
}
if (cellState.shape[0] !== batchSize || cellState.shape[1] !== hiddenSize) {
throw new Error(`The shape of cellState [${cellState.shape[0]},
${cellState.shape[1]}] is invalid.`);
}
if (bias) {
if (bias.rank !== 1) {
throw new Error(`The bias (rank ${bias.rank}) is not a 1-D tensor.`);
}
if (bias.shape[0] !== 4 * hiddenSize) {
throw new Error(`The shape of bias [${bias.shape[0]}] is invalid.`);
}
}
if (recurrentBias) {
if (recurrentBias.rank !== 1) {
throw new Error(`The recurrentBias (rank ${bias.rank}) is not a 1-D tensor.`);
}
if (recurrentBias.shape[0] !== 4 * hiddenSize) {
throw new Error(`The shape of recurrentBias [${recurrentBias.shape[0]}] is invalid.`);
}
}
if (peepholeWeight) {
if (peepholeWeight.rank !== 1) {
throw new Error(`The peepholeWeight (rank ${bias.rank}) is not a 1-D tensor.`);
}
if (peepholeWeight.shape[0] !== 3 * hiddenSize) {
throw new Error(`The shape of peepholeWeight [${peepholeWeight.shape[0]}] is invalid.`);
}
}
if (layout !== 'iofg' && layout !== 'ifgo') {
throw new Error(`The layout ${layout} is invalid.`);
}
}

export function validateLstmParams(input, weight, recurrentWeight, steps, hiddenSize,
{bias, recurrentBias, peepholeWeight, initialHiddenState, initialCellState,
direction = 'forward', layout = 'iofg'}) {
if (!Number.isInteger(steps) || steps <= 0) {
throw new Error(`The steps ${steps} is invalid.`);
}
if (!Number.isInteger(hiddenSize) || hiddenSize <= 0) {
throw new Error(`The hiddenSize ${hiddenSize} is invalid.`);
}
if (input.rank !== 3) {
throw new Error(`The input (rank ${input.rank}) is not a 3-D tensor.`);
}
if (input.shape[0] !== steps) {
throw new Error(`The input.shape[0] ${input.shape[0]} is not equal to steps ${steps}.`);
}
const batchSize = input.shape[1];
const inputSize = input.shape[2];
if (direction !== 'forward' && direction !== 'backward' && direction !== 'both') {
throw new Error(`The direction ${direction} is invalid.`);
}
const numDirections = (direction === 'both' ? 2 : 1);
if (weight.rank !== 3) {
throw new Error(`The weight (rank ${weight.rank}) is not a 3-D tensor.`);
}
if (weight.shape[0] !== numDirections || weight.shape[1] !== 4 * hiddenSize ||
weight.shape[2] !== inputSize) {
throw new Error(`The shape of weight [${weight.shape[0]}, ${weight.shape[1]},
${weight.shape[2]}] is invalid.`);
}
if (recurrentWeight.rank !== 3) {
throw new Error(`The recurrentWeight (rank ${recurrentWeight.rank}) is not a 3-D tensor.`);
}
if (recurrentWeight.shape[0] !== numDirections ||
recurrentWeight.shape[1] !== 4 * hiddenSize ||
recurrentWeight.shape[2] !== hiddenSize) {
throw new Error(`The shape of recurrentWeight ` +
`[${recurrentWeight.shape[0]}, ${recurrentWeight.shape[1]}, ` +
`${recurrentWeight.shape[2]}] is invalid.`);
}
if (bias) {
if (bias.rank !== 2) {
throw new Error(`The bias (rank ${bias.rank}) is not a 2-D tensor.`);
}
if (bias.shape[0] !== numDirections || bias.shape[1] !== 4 * hiddenSize) {
throw new Error(`The shape of bias [${bias.shape[0]}, ${bias.shape[1]}] is invalid.`);
}
}
if (recurrentBias) {
if (recurrentBias.rank !== 2) {
throw new Error(`The recurrentBias (rank ${recurrentBias.rank}) is not a 2-D tensor.`);
}
if (recurrentBias.shape[0] !== numDirections || recurrentBias.shape[1] !== 4 * hiddenSize) {
throw new Error(`The shape of recurrentBias [${recurrentBias.shape[0]},
${recurrentBias.shape[1]}] is invalid.`);
}
}
if (peepholeWeight) {
if (peepholeWeight.rank !== 2) {
throw new Error(`The peepholeWeight (rank ${peepholeWeight.rank}) is not a 2-D tensor.`);
}
if (peepholeWeight.shape[0] !== numDirections || peepholeWeight.shape[1] !== 3 * hiddenSize) {
throw new Error(`The shape of peepholeWeight [${peepholeWeight.shape[0]},
${peepholeWeight.shape[1]}] is invalid.`);
}
}
if (initialHiddenState) {
if (initialHiddenState.rank !== 3) {
throw new Error(
`The initialHiddenState (rank ${initialHiddenState.rank}) is not a 3-D tensor.`);
}
if (initialHiddenState.shape[0] !== numDirections ||
initialHiddenState.shape[1] !== batchSize ||
initialHiddenState.shape[2] !== hiddenSize) {
throw new Error(`The shape of initialHiddenState [${initialHiddenState.shape[0]},
${initialHiddenState.shape[1]}, ${initialHiddenState.shape[2]}] is invalid.`);
}
}
if (initialCellState) {
if (initialCellState.rank !== 3) {
throw new Error(
`The initialCellState (rank ${initialCellState.rank}) is not a 3-D tensor.`);
}
if (initialCellState.shape[0] !== numDirections ||
initialCellState.shape[1] !== batchSize ||
initialCellState.shape[2] !== hiddenSize) {
throw new Error(`The shape of initialCellState [${initialCellState.shape[0]},
${initialCellState.shape[1]}, ${initialCellState.shape[2]}] is invalid.`);
}
}
if (layout !== 'iofg' && layout !== 'ifgo') {
throw new Error(`The layout ${layout} is invalid.`);
}
}


export function validateGruCellParams(input, weight, recurrentWeight, hiddenState, hiddenSize,
{bias, recurrentBias, layout = 'zrn'} = {}) {
if (!Number.isInteger(hiddenSize) || hiddenSize <= 0) {
Expand Down Expand Up @@ -160,8 +322,8 @@ export function validateGruCellParams(input, weight, recurrentWeight, hiddenStat
throw new Error(`The hiddenState (rank ${hiddenState.rank}) is not a 2-D tensor.`);
}
if (hiddenState.shape[0] !== batchSize || hiddenState.shape[1] !== hiddenSize) {
throw new Error(`The shape of hiddenState
[${hiddenState.shape[0]}, ${hiddenState.shape[1]}] is invalid.`);
throw new Error(`The shape of hiddenState [${hiddenState.shape[0]},
${hiddenState.shape[1]}] is invalid.`);
}
if (bias) {
if (bias.rank !== 1) {
Expand Down
107 changes: 107 additions & 0 deletions src/lstm.js
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Original file line number Diff line number Diff line change
@@ -0,0 +1,107 @@
'use strict';

import {concat} from './concat.js';
import {lstmCell} from './lstm_cell.js';
import {reshape} from './reshape.js';
import {sizeOfShape, Tensor} from './lib/tensor.js';
import {sigmoid} from './sigmoid.js';
import {slice} from './slice.js';
import {squeeze} from './squeeze.js';
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Now squeeze op has been removed, would you please help also remove it from this WebNN Baseline.
You can refer to the given squeeze method in Spec, thanks.

function squeeze(builder, op) {
  return builder.reshape(op, op.shape().remove(0));
}

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The definition is:

function squeeze(input, axes) {
  if (!axes) axes = [];
  if (!axes.length)
    input.shape().forEach((item, i) => { axes.push(i); });
  shape = Array.from(input.shape());
  for (let axis in axes.sort().reverse())
    if (axis < shape.length && shape[axis] == 1)
      shape.splice(axis, 1);
  return builder.reshape(input, shape);
}

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OK， I will revise it next week:)

import {tanh} from './tanh.js';
import {validateLstmParams} from './lib/validate-input.js';

/**
*Long Short-Term Memory [LSTM] recurrent network uses an input, output, forget,
*and cell gate to compute the output state that rolls into the output across the
* temporal sequence of the network.
* @param {Tensor} input
* @param {Tensor} weight
* @param {Tensor} recurrentWeight
* @param {Number} steps
* @param {Number} hiddenSize
* @param {MLLstmOptions} options
* @return {Array.<Tensor>}
*/
export function lstm(input, weight, recurrentWeight, steps, hiddenSize,
{bias, recurrentBias, peepholeWeight, initialHiddenState,
initialCellState, returnSequence = false, direction = 'forward', layout = 'iofg',
activations = [sigmoid, tanh, tanh]} = {}) {
validateLstmParams(...arguments);
const numDirections = (direction == 'both' ? 2 : 1);
const batchSize = input.shape[1];
const inputSize = input.shape[2];

let hiddenState;
let cellState;
if (initialHiddenState) {
hiddenState = initialHiddenState;
} else {
const initialHiddenStateShape = [numDirections, batchSize, hiddenSize];
hiddenState = new Tensor(
initialHiddenStateShape, new Array(sizeOfShape(initialHiddenStateShape)).fill(0));
}
if (initialCellState) {
cellState = initialCellState;
} else {
const initialCellState = [numDirections, batchSize, hiddenSize];
cellState = new Tensor(
initialCellState, new Array(sizeOfShape(initialCellState)).fill(0));
}

let sequence;
const currentWeight = [];
const currentRecurrentWeight = [];
const currentBias = [];
const currentRecurrentBias = [];
const currentPeepholeWeight = [];

for (let dir = 0; dir < numDirections; ++dir) {
currentWeight.push(squeeze(slice(weight, [dir, 0, 0], [1, 4 * hiddenSize, inputSize])));
currentRecurrentWeight.push(squeeze(slice(recurrentWeight,
[dir, 0, 0], [1, 4 * hiddenSize, hiddenSize])));
currentBias.push(bias ? (squeeze(slice(bias, [dir, 0], [1, 4 * hiddenSize]))) : null);
currentRecurrentBias.push(recurrentBias ?
(squeeze(slice(recurrentBias, [dir, 0], [1, 4 * hiddenSize]))) : null);
currentPeepholeWeight.push(peepholeWeight ?
(squeeze(slice(peepholeWeight, [dir, 0], [1, 3 * hiddenSize]))) : null);
}

for (let step = 0; step < steps; ++step) {
const currentHidden = [];
const currentCell = [];
let nextHidden = null;
let nextCell = null;

for (let dir = 0; dir < numDirections; ++dir) {
currentHidden.push(squeeze(slice(hiddenState, [dir, 0, 0], [1, batchSize, hiddenSize])));
currentCell.push(squeeze(slice(cellState, [dir, 0, 0], [1, batchSize, hiddenSize])));
}

for (let dir = 0; dir < numDirections; ++dir) {
const slice0 = (dir == 1 || direction == 'backward' ? steps - step - 1 : step);
const currentInput = squeeze(slice(input, [slice0, 0, 0], [1, batchSize, inputSize]));

const results = lstmCell(
currentInput, currentWeight[dir], currentRecurrentWeight[dir],
currentHidden[dir], currentCell[dir], hiddenSize, {bias: currentBias[dir],
recurrentBias: currentRecurrentBias[dir], peepholeWeight: currentPeepholeWeight[dir],
layout: layout, activations: activations});

const output = reshape(results[0], [1, null, hiddenSize]);
const cell = reshape(results[1], [1, null, hiddenSize]);

nextHidden = (nextHidden ? concat([nextHidden, output], 0) : output);
nextCell = (nextCell ? concat([nextCell, cell], 0) : cell);
}

hiddenState = nextHidden;
cellState = nextCell;

if (returnSequence) {
nextHidden = reshape(nextHidden, [1, numDirections, null, hiddenSize]);
sequence = (sequence ? concat([sequence, nextHidden], 0) : nextHidden);
}
}

return (sequence ? [hiddenState, cellState, sequence] : [hiddenState, cellState]);
}
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