/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import { util } from '@tensorflow/tfjs-core';
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import { Im2ColPackedProgram } from '../im2col_packed_gpu';
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import { mapActivationToShaderProgram } from '../kernel_utils/kernel_funcs_utils';
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import { MatMulPackedProgram } from '../mulmat_packed_gpu';
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import * as webgl_util from '../webgl_util';
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import { batchMatMulImpl, MATMUL_SHARED_DIM_THRESHOLD } from './BatchMatMul_impl';
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import { identity } from './Identity';
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import { reshape } from './Reshape';
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// Both conv2dByMatMul and conv2dWithIm2Row fuse height and width into one
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// dimension to compute batchMatMul, so bias and activation weights are also
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// supposed to fuse the two dimensions into one.
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//
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// This function computes the target shape for fusing height and width
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// dimensions. Returning null means the shape is already compatible.
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//
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// Even though the bias is not supposed to be a 3-D or a 4-D (including
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// batch) tensor and PReLU activiation weights is not supposed to be a 4-D
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// tensor, we still need to support them, because we haven't disabled
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// them for NHWC format.
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// https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/conv2d.ts#L181-L196
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function getShapeForBatchMatMul(shape, isChannelsLast) {
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const length = shape.length;
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if (length >= 3) {
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return isChannelsLast ?
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[
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...shape.slice(0, -3) /* batch */,
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shape[length - 3] * shape[length - 2] /* height * width */,
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shape[length - 1] /* channel */
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] :
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[
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...shape.slice(0, -3) /* batch */, shape[length - 3] /* channel */,
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shape[length - 2] * shape[length - 1] /* height * width */
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];
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}
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else if (!isChannelsLast && length === 1 && shape[0] > 1) {
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return [shape[0], 1];
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}
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else {
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return null;
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}
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}
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// For 1x1 kernels that iterate through every point in the input, convolution
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// can be expressed as matrix multiplication (without need for memory
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// remapping).
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export function conv2dByMatMul({ x, filter, convInfo, backend, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation = null }) {
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// Reshapes conv2D input to 2D tensors, uses matMul and then reshape the
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// result from 2D to 4D.
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const xShape = x.shape;
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const xTexData = backend.texData.get(x.dataId);
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const sharedMatMulDim = convInfo.inChannels;
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const outerShapeX = xShape[0] * xShape[1] * xShape[2];
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const outerShapeFilter = convInfo.outChannels;
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const isChannelsLast = convInfo.dataFormat === 'channelsLast';
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const transposeA = false;
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const transposeB = false;
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let out;
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const intermediates = [];
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if (preluActivationWeights != null) {
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const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
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if (targetShape != null) {
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preluActivationWeights = reshape({
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inputs: { x: preluActivationWeights },
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backend,
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attrs: { shape: targetShape }
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});
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intermediates.push(preluActivationWeights);
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}
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}
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if (bias != null) {
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const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
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if (targetShape != null) {
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bias = reshape({ inputs: { x: bias }, backend, attrs: { shape: targetShape } });
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intermediates.push(bias);
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}
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}
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// TODO: Once reduction ops are packed, batchMatMul will always be packed
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// and we can remove this condition.
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const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) &&
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sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;
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// The algorithm in the if condition assumes (1) the output will be packed,
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// (2) x is packed, (3) x isChannelsLast, (4) x's packed texture is already
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// on GPU, (5) col is odd, (6) the width, height and inChannels are the same
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// for xTexData.shape and xShape.
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const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked &&
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isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 &&
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util.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));
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if (canOptimize) {
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// We avoid expensive packed 2x2 reshape by padding col count to next,
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// even number. When col is odd, the result of packed batchMatMul is
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// the same (has the same texture layout and and values in the texture) as
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// it is for next even col. We make the odd-cols tensor to look like
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// even-cols tensor before the operation and, after the batchMatMul,
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// fix the even-cols result to have odd number of cols.
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const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);
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const xReshaped = {
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dataId: x.dataId,
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shape: [1, targetShape, convInfo.inChannels],
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dtype: x.dtype
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};
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// xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.
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// Decrementing col count, after batchMatMul->...->compileProgram leads to
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// invalid col count within the reference in GPGPUBinary.inShapeInfos.
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// Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos
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// in compileProgram method, but that would affect compilation of all
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// programs - instead, provide a copy here, with even col count, before
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// calling batchMatMul->...->compileProgram and after that, the original
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// xTexData.shape is restored.
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const originalXTexDataShape = xTexData.shape;
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xTexData.shape = xTexData.shape.slice();
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xTexData.shape[xTexData.shape.length - 2]++;
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util.assert(webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);
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const filterReshaped = reshape({
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inputs: { x: filter },
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backend,
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attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
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});
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intermediates.push(filterReshaped);
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const pointwiseConv = batchMatMulImpl({
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a: xReshaped,
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b: filterReshaped,
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backend,
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transposeA,
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transposeB,
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bias,
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activation,
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preluActivationWeights,
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leakyreluAlpha
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});
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const pointwiseConvTexData = backend.texData.get(pointwiseConv.dataId);
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util.assert(pointwiseConvTexData.isPacked, () => 'batchMatMul result is expected to be packed');
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// Restore the input shape to original.
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xTexData.shape = originalXTexDataShape;
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// Set the output shape - there is no need for expensive reshape as data
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// layout is already correct.
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pointwiseConvTexData.shape = convInfo.outShape;
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out = identity({ inputs: { x: pointwiseConv }, backend });
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out.shape = convInfo.outShape;
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intermediates.push(pointwiseConv);
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}
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else {
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const numCols = convInfo.outHeight * convInfo.outWidth;
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const xReshaped = reshape({
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inputs: { x },
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backend,
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attrs: {
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shape: isChannelsLast ?
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[convInfo.batchSize, numCols, convInfo.inChannels] :
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[convInfo.batchSize, convInfo.inChannels, numCols]
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}
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});
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const filterReshaped = reshape({
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inputs: { x: filter },
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backend,
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attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
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});
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const result = batchMatMulImpl({
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a: isChannelsLast ? xReshaped : filterReshaped,
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b: isChannelsLast ? filterReshaped : xReshaped,
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transposeA: !isChannelsLast,
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transposeB,
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backend,
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bias,
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activation,
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preluActivationWeights,
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leakyreluAlpha
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});
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out = reshape({ inputs: { x: result }, backend, attrs: { shape: convInfo.outShape } });
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intermediates.push(xReshaped);
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intermediates.push(filterReshaped);
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intermediates.push(result);
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}
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for (const i of intermediates) {
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backend.disposeIntermediateTensorInfo(i);
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}
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return out;
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}
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// Implements the im2row algorithm as outlined in "High Performance
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// Convolutional Neural Networks for Document Processing" (Suvisoft, 2006)
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export function conv2dWithIm2Row({ x, filter, convInfo, backend, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation = null }) {
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// Rearranges conv2d input so each block to be convolved over forms the
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// column of a new matrix with shape [filterWidth * filterHeight *
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// inChannels, outHeight * outWidth]. The filter is also rearranged so each
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// output channel forms a row of a new matrix with shape [outChannels,
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// filterWidth * filterHeight * inChannels]. The convolution is then
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// computed by multiplying these matrices and reshaping the result.
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const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;
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const isChannelsLast = dataFormat === 'channelsLast';
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const sharedDim = filterWidth * filterHeight * inChannels;
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const numCols = outHeight * outWidth;
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const x2ColShape = [convInfo.batchSize, sharedDim, numCols];
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const transposeA = true;
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const transposeB = false;
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const intermediates = [];
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if (preluActivationWeights != null) {
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const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
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if (targetShape != null) {
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preluActivationWeights = reshape({
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inputs: { x: preluActivationWeights },
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backend,
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attrs: { shape: targetShape }
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});
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intermediates.push(preluActivationWeights);
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}
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}
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if (bias != null) {
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const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
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if (targetShape != null) {
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bias = reshape({ inputs: { x: bias }, backend, attrs: { shape: targetShape } });
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intermediates.push(bias);
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}
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}
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const w2Row = reshape({
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inputs: { x: filter },
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backend,
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attrs: { shape: [1, sharedDim, util.sizeFromShape(filter.shape) / sharedDim] }
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});
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intermediates.push(w2Row);
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const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);
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const customValues = [
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x.shape, [convInfo.padInfo.top, convInfo.padInfo.left],
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[convInfo.strideHeight, convInfo.strideWidth],
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[convInfo.dilationHeight, convInfo.dilationWidth], [convInfo.inChannels],
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[convInfo.filterWidth * convInfo.inChannels], [convInfo.outWidth]
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];
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const im2Col = backend.runWebGLProgram(im2ColProgram, [x], 'float32', customValues);
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const im2ColReshaped = reshape({ inputs: { x: im2Col }, backend, attrs: { shape: x2ColShape } });
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intermediates.push(im2Col);
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intermediates.push(im2ColReshaped);
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const hasBias = bias != null;
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const hasPreluActivationWeights = preluActivationWeights != null;
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const hasLeakyreluAlpha = activation === 'leakyrelu';
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const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;
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const matmulProgram = new MatMulPackedProgram(isChannelsLast ? im2ColReshaped.shape :
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w2Row.shape, isChannelsLast ? w2Row.shape :
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im2ColReshaped.shape, isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] :
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[convInfo.batchSize, convInfo.outChannels, numCols], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
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const inputs = isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped];
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if (bias) {
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inputs.push(bias);
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}
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if (hasPreluActivationWeights) {
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inputs.push(preluActivationWeights);
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}
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if (hasLeakyreluAlpha) {
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const $leakyreluAlpha = backend.makeTensorInfo([], 'float32', util.createScalarValue(leakyreluAlpha, 'float32'));
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inputs.push($leakyreluAlpha);
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intermediates.push($leakyreluAlpha);
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}
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const product = backend.runWebGLProgram(matmulProgram, inputs, 'float32');
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const out = reshape({ inputs: { x: product }, backend, attrs: { shape: convInfo.outShape } });
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intermediates.push(product);
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for (const i of intermediates) {
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backend.disposeIntermediateTensorInfo(i);
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}
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return out;
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}
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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, TensorInfo, util} from '@tensorflow/tfjs-core';\n\n// import {assertAndGetBroadcastShape} from\n// '../../../tfjs-core/src/ops/broadcast_util';\nimport {MathBackendWebGL} from '../backend_webgl';\nimport {Im2ColPackedProgram} from '../im2col_packed_gpu';\nimport {mapActivationToShaderProgram} from '../kernel_utils/kernel_funcs_utils';\nimport {MatMulPackedProgram} from '../mulmat_packed_gpu';\nimport * as webgl_util from '../webgl_util';\n\nimport {batchMatMulImpl, MATMUL_SHARED_DIM_THRESHOLD} from './BatchMatMul_impl';\nimport {identity} from './Identity';\nimport {reshape} from './Reshape';\n\ntype Conv2DConfig = {\n  x: TensorInfo,\n  filter: TensorInfo,\n  convInfo: backend_util.Conv2DInfo,\n  backend: MathBackendWebGL,\n  bias?: TensorInfo,\n  preluActivationWeights?: TensorInfo,\n  leakyreluAlpha?: number,\n  activation?: backend_util.Activation\n};\n\n// Both conv2dByMatMul and conv2dWithIm2Row fuse height and width into one\n// dimension to compute batchMatMul, so bias and activation weights are also\n// supposed to fuse the two dimensions into one.\n//\n// This function computes the target shape for fusing height and width\n// dimensions. Returning null means the shape is already compatible.\n//\n// Even though the bias is not supposed to be a 3-D or a 4-D (including\n// batch) tensor and PReLU activiation weights is not supposed to be a 4-D\n// tensor, we still need to support them, because we haven't disabled\n// them for NHWC format.\n// https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/conv2d.ts#L181-L196\nfunction getShapeForBatchMatMul(\n    shape: number[], isChannelsLast: boolean): number[] {\n  const length = shape.length;\n  if (length >= 3) {\n    return isChannelsLast ?\n        [\n          ...shape.slice(0, -3) /* batch */,\n          shape[length - 3] * shape[length - 2] /* height * width */,\n          shape[length - 1] /* channel */\n        ] :\n        [\n          ...shape.slice(0, -3) /* batch */, shape[length - 3] /* channel */,\n          shape[length - 2] * shape[length - 1] /* height * width */\n        ];\n  } else if (!isChannelsLast && length === 1 && shape[0] > 1) {\n    return [shape[0], 1];\n  } else {\n    return null;\n  }\n}\n\n// For 1x1 kernels that iterate through every point in the input, convolution\n// can be expressed as matrix multiplication (without need for memory\n// remapping).\nexport function conv2dByMatMul({\n  x,\n  filter,\n  convInfo,\n  backend,\n  bias = null,\n  preluActivationWeights = null,\n  leakyreluAlpha = 0,\n  activation = null\n}: Conv2DConfig) {\n  // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the\n  // result from 2D to 4D.\n  const xShape = x.shape;\n  const xTexData = backend.texData.get(x.dataId);\n  const sharedMatMulDim = convInfo.inChannels;\n  const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n  const outerShapeFilter = convInfo.outChannels;\n  const isChannelsLast = convInfo.dataFormat === 'channelsLast';\n  const transposeA = false;\n  const transposeB = false;\n\n  let out: TensorInfo;\n  const intermediates: TensorInfo[] = [];\n\n  if (preluActivationWeights != null) {\n    const targetShape =\n        getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);\n    if (targetShape != null) {\n      preluActivationWeights = reshape({\n        inputs: {x: preluActivationWeights},\n        backend,\n        attrs: {shape: targetShape}\n      });\n      intermediates.push(preluActivationWeights);\n    }\n  }\n\n  if (bias != null) {\n    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);\n    if (targetShape != null) {\n      bias = reshape({inputs: {x: bias}, backend, attrs: {shape: targetShape}});\n      intermediates.push(bias);\n    }\n  }\n\n  // TODO: Once reduction ops are packed, batchMatMul will always be packed\n  // and we can remove this condition.\n  const batchMatMulWillBeUnpacked =\n      (outerShapeX === 1 || outerShapeFilter === 1) &&\n      sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n\n  // The algorithm in the if condition assumes (1) the output will be packed,\n  // (2) x is packed, (3) x isChannelsLast, (4)  x's packed texture is already\n  // on GPU, (5) col is odd, (6) the width, height and inChannels are the same\n  // for xTexData.shape and xShape.\n  const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked &&\n      isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 &&\n      util.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));\n\n  if (canOptimize) {\n    // We avoid expensive packed 2x2 reshape by padding col count to next,\n    // even number. When col is odd, the result of packed batchMatMul is\n    // the same (has the same texture layout and and values in the texture) as\n    // it is for next even col. We make the odd-cols tensor to look like\n    // even-cols tensor before the operation and, after the batchMatMul,\n    // fix the even-cols result to have odd number of cols.\n    const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);\n    const xReshaped: TensorInfo = {\n      dataId: x.dataId,\n      shape: [1, targetShape, convInfo.inChannels],\n      dtype: x.dtype\n    };\n    // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.\n    // Decrementing col count, after batchMatMul->...->compileProgram leads to\n    // invalid col count within the reference in GPGPUBinary.inShapeInfos.\n    // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos\n    // in compileProgram method, but that would affect compilation of all\n    // programs - instead, provide a copy here, with even col count, before\n    // calling batchMatMul->...->compileProgram and after that, the original\n    // xTexData.shape is restored.\n    const originalXTexDataShape = xTexData.shape;\n    xTexData.shape = xTexData.shape.slice();\n    xTexData.shape[xTexData.shape.length - 2]++;\n    util.assert(\n        webgl_util.isReshapeFree(xTexData.shape, xReshaped.shape),\n        () => `packed reshape ${xTexData.shape} to ${\n            xReshaped.shape} isn't free`);\n    const filterReshaped = reshape({\n      inputs: {x: filter},\n      backend,\n      attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}\n    });\n    intermediates.push(filterReshaped);\n    const pointwiseConv = batchMatMulImpl({\n      a: xReshaped,\n      b: filterReshaped,\n      backend,\n      transposeA,\n      transposeB,\n      bias,\n      activation,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n\n    const pointwiseConvTexData = backend.texData.get(pointwiseConv.dataId);\n    util.assert(\n        pointwiseConvTexData.isPacked,\n        () => 'batchMatMul result is expected to be packed');\n    // Restore the input shape to original.\n    xTexData.shape = originalXTexDataShape;\n    // Set the output shape - there is no need for expensive reshape as data\n    // layout is already correct.\n    pointwiseConvTexData.shape = convInfo.outShape;\n\n    out = identity({inputs: {x: pointwiseConv}, backend});\n    out.shape = convInfo.outShape;\n\n    intermediates.push(pointwiseConv);\n  } else {\n    const numCols = convInfo.outHeight * convInfo.outWidth;\n    const xReshaped = reshape({\n      inputs: {x},\n      backend,\n      attrs: {\n        shape: isChannelsLast ?\n            [convInfo.batchSize, numCols, convInfo.inChannels] :\n            [convInfo.batchSize, convInfo.inChannels, numCols]\n      }\n    });\n    const filterReshaped = reshape({\n      inputs: {x: filter},\n      backend,\n      attrs: {shape: [1, convInfo.inChannels, convInfo.outChannels]}\n    });\n    const result = batchMatMulImpl({\n      a: isChannelsLast ? xReshaped : filterReshaped,\n      b: isChannelsLast ? filterReshaped : xReshaped,\n      transposeA: !isChannelsLast,\n      transposeB,\n      backend,\n      bias,\n      activation,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n\n    out = reshape(\n        {inputs: {x: result}, backend, attrs: {shape: convInfo.outShape}});\n\n    intermediates.push(xReshaped);\n    intermediates.push(filterReshaped);\n    intermediates.push(result);\n  }\n\n  for (const i of intermediates) {\n    backend.disposeIntermediateTensorInfo(i);\n  }\n\n  return out;\n}\n\n// Implements the im2row algorithm as outlined in \"High Performance\n// Convolutional Neural Networks for Document Processing\" (Suvisoft, 2006)\nexport function conv2dWithIm2Row({\n  x,\n  filter,\n  convInfo,\n  backend,\n  bias = null,\n  preluActivationWeights = null,\n  leakyreluAlpha = 0,\n  activation = null\n}: Conv2DConfig) {\n  // Rearranges conv2d input so each block to be convolved over forms the\n  // column of a new matrix with shape [filterWidth * filterHeight *\n  // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n  // output channel forms a row of a new matrix with shape [outChannels,\n  // filterWidth * filterHeight * inChannels]. The convolution is then\n  // computed by multiplying these matrices and reshaping the result.\n  const {\n    filterWidth,\n    filterHeight,\n    inChannels,\n    outWidth,\n    outHeight,\n    dataFormat\n  } = convInfo;\n\n  const isChannelsLast = dataFormat === 'channelsLast';\n\n  const sharedDim = filterWidth * filterHeight * inChannels;\n  const numCols = outHeight * outWidth;\n  const x2ColShape = [convInfo.batchSize, sharedDim, numCols];\n  const transposeA = true;\n  const transposeB = false;\n\n  const intermediates: TensorInfo[] = [];\n\n  if (preluActivationWeights != null) {\n    const targetShape =\n        getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);\n    if (targetShape != null) {\n      preluActivationWeights = reshape({\n        inputs: {x: preluActivationWeights},\n        backend,\n        attrs: {shape: targetShape}\n      });\n      intermediates.push(preluActivationWeights);\n    }\n  }\n\n  if (bias != null) {\n    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);\n    if (targetShape != null) {\n      bias = reshape({inputs: {x: bias}, backend, attrs: {shape: targetShape}});\n      intermediates.push(bias);\n    }\n  }\n\n  const w2Row = reshape({\n    inputs: {x: filter},\n    backend,\n    attrs: {shape: [1, sharedDim, util.sizeFromShape(filter.shape) / sharedDim]}\n  });\n  intermediates.push(w2Row);\n\n  const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);\n  const customValues = [\n    x.shape, [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth], [convInfo.inChannels],\n    [convInfo.filterWidth * convInfo.inChannels], [convInfo.outWidth]\n  ];\n  const im2Col =\n      backend.runWebGLProgram(im2ColProgram, [x], 'float32', customValues);\n  const im2ColReshaped =\n      reshape({inputs: {x: im2Col}, backend, attrs: {shape: x2ColShape}});\n\n  intermediates.push(im2Col);\n  intermediates.push(im2ColReshaped);\n\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation === 'leakyrelu';\n  const fusedActivation =\n      activation ? mapActivationToShaderProgram(activation, true) : null;\n  const matmulProgram = new MatMulPackedProgram(\n      isChannelsLast ? im2ColReshaped.shape as [number, number, number] :\n                       w2Row.shape as [number, number, number],\n      isChannelsLast ? w2Row.shape as [number, number, number] :\n                       im2ColReshaped.shape as [number, number, number],\n      isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] :\n                       [convInfo.batchSize, convInfo.outChannels, numCols],\n      transposeA, transposeB, hasBias, fusedActivation,\n      hasPreluActivationWeights, hasLeakyreluAlpha);\n  const inputs: TensorInfo[] =\n      isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped];\n  if (bias) {\n    inputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    inputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend.makeTensorInfo(\n        [], 'float32',\n        util.createScalarValue(leakyreluAlpha as unknown as 'float32',\n                               'float32'));\n    inputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n  const product = backend.runWebGLProgram(matmulProgram, inputs, 'float32');\n  const out = reshape(\n      {inputs: {x: product}, backend, attrs: {shape: convInfo.outShape}});\n\n  intermediates.push(product);\n  for (const i of intermediates) {\n    backend.disposeIntermediateTensorInfo(i);\n  }\n\n  return out;\n}\n"]}
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