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completed lstm #71
completed lstm #71
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Original file line number | Diff line number | Diff line change | ||||
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'use strict'; | ||||||
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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'; | ||||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Now squeeze op has been removed, would you please help also remove it from this WebNN Baseline.
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. The definition is:
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. OK, I will revise it next week:) |
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import {tanh} from './tanh.js'; | ||||||
import {validateLstmParams} from './lib/validate-input.js'; | ||||||
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/** | ||||||
*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>} | ||||||
*/ | ||||||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Please delete this blank line. |
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export function lstm(input, weight, recurrentWeight, steps, hiddenSize, | ||||||
{bias, recurrentBias, peepholeWeight, initialHiddenState, | ||||||
initialCellState, returnSequence = false, direction = 'forward', layout = 'iofg', | ||||||
activations = [sigmoid, tanh, tanh]}={}) { | ||||||
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Suggested change
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validateLstmParams(...arguments); | ||||||
const numDirections = (direction == 'both' ? 2 : 1); | ||||||
const batchSize = input.shape[1]; | ||||||
const inputSize = input.shape[2]; | ||||||
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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)); | ||||||
} | ||||||
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let sequence; | ||||||
const currentWeight = []; | ||||||
const currentRecurrentWeight = []; | ||||||
const currentBias = []; | ||||||
const currentRecurrentBias = []; | ||||||
const currentPeepholeWeight = []; | ||||||
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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); | ||||||
} | ||||||
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for (let step = 0; step < steps; ++step) { | ||||||
const currentHidden = []; | ||||||
const currentCell = []; | ||||||
let nextHidden = null; | ||||||
let nextCell = null; | ||||||
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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]))); | ||||||
} | ||||||
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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])); | ||||||
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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}); | ||||||
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const output = reshape(results[0], [1, null, hiddenSize]); | ||||||
const cell = reshape(results[1], [1, null, hiddenSize]); | ||||||
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nextHidden = (nextHidden ? concat([nextHidden, output], 0) : output); | ||||||
nextCell = (nextCell ? concat([nextCell, cell], 0) : cell); | ||||||
} | ||||||
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hiddenState = nextHidden; | ||||||
cellState = nextCell; | ||||||
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if (returnSequence) { | ||||||
nextHidden = reshape(nextHidden, [1, numDirections, null, hiddenSize]); | ||||||
sequence = (sequence ? concat([sequence, nextHidden], 0) : nextHidden); | ||||||
} | ||||||
} | ||||||
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return (sequence ? [hiddenState, cellState, sequence] : [hiddenState, cellState]); | ||||||
} |
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The error message will be split :( :