/**
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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 { BatchMatMul, broadcast_util, buffer, util } from '@tensorflow/tfjs-core';
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import { assertNotComplex } from '../cpu_util';
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import { reshape } from './Reshape';
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export function batchMatMul(args) {
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const { inputs, backend, attrs } = args;
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const { a, b } = inputs;
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const { transposeA, transposeB } = attrs;
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assertNotComplex([a, b], 'matMul');
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const aRank = a.shape.length;
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const bRank = b.shape.length;
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const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
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const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
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const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
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const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
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const outerDimsA = a.shape.slice(0, -2);
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const outerDimsB = b.shape.slice(0, -2);
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const batchDimA = util.sizeFromShape(outerDimsA);
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const batchDimB = util.sizeFromShape(outerDimsB);
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const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));
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const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
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util.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (` +
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`${innerShapeB}) of Tensors with shapes ${a.shape} and ` +
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`${b.shape} and transposeA=${transposeA}` +
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` and transposeB=${transposeB} must match.`);
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const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] :
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[batchDimA, outerShapeA, innerShapeA];
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const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] :
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[batchDimB, innerShapeB, outerShapeB];
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// The rest of the implementation is designed to operate on rank-3 tensors
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const a3d = reshape({ inputs: { x: a }, backend, attrs: { shape: a3dShape } });
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const b3d = reshape({ inputs: { x: b }, backend, attrs: { shape: b3dShape } });
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const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
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const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
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const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
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const batchDim = Math.max(batchDimA, batchDimB);
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const a3dValues = backend.data.get(a3d.dataId).values;
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const b3dValues = backend.data.get(b3d.dataId).values;
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const a3dStrides = util.computeStrides(a3d.shape);
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const b3dStrides = util.computeStrides(b3d.shape);
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const [aBatch, aOuterStep, aInnerStep] = transposeA ?
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[a3dStrides[0], 1, a3dStrides[1]] :
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[a3dStrides[0], a3dStrides[1], 1];
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const [bInnerStep, bOuterStep, bBatch] = transposeB ?
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[1, b3dStrides[1], b3dStrides[0]] :
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[b3dStrides[1], 1, b3dStrides[0]];
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const size = leftDim * rightDim;
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const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);
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const resVals = result.values;
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const blockSize = backend.blockSize;
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for (let bi = 0; bi < batchDim; bi++) {
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const batchIndexA = bi % batchDimA;
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const batchIndexB = bi % batchDimB;
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for (let i0 = 0; i0 < leftDim; i0 += blockSize) {
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// for when blockSize doesn't evenly divide the input
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const iBlock = Math.min(i0 + blockSize, leftDim);
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for (let j0 = 0; j0 < rightDim; j0 += blockSize) {
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const jBlock = Math.min(j0 + blockSize, rightDim);
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for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {
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const kBlock = Math.min(k0 + blockSize, sharedDim);
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for (let i = i0; i < iBlock; i++) {
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for (let j = j0; j < jBlock; j++) {
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let sum = 0.0;
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for (let k = k0; k < kBlock; k++) {
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const aVal =
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// tslint:disable-next-line: max-line-length
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a3dValues[batchIndexA * aBatch + i * aOuterStep + k * aInnerStep];
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const bVal =
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// tslint:disable-next-line: max-line-length
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b3dValues[k * bInnerStep + j * bOuterStep + batchIndexB * bBatch];
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sum += aVal * bVal;
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}
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resVals[bi * size + (i * rightDim + j)] += sum;
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}
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}
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}
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}
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}
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}
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backend.disposeIntermediateTensorInfo(a3d);
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backend.disposeIntermediateTensorInfo(b3d);
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// set correct shape on output.
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return backend.makeTensorInfo(outShape, result.dtype, result.values);
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}
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export const batchMatMulConfig = {
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kernelName: BatchMatMul,
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backendName: 'cpu',
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kernelFunc: batchMatMul,
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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 {BatchMatMul, BatchMatMulAttrs, BatchMatMulInputs, broadcast_util, buffer, KernelConfig, KernelFunc, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nimport {reshape} from './Reshape';\n\nexport function batchMatMul(args: {\n  inputs: BatchMatMulInputs,\n  attrs: BatchMatMulAttrs,\n  backend: MathBackendCPU\n}) {\n  const {inputs, backend, attrs} = args;\n  const {a, b} = inputs;\n  const {transposeA, transposeB} = attrs;\n\n  assertNotComplex([a, b], 'matMul');\n\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n\n  const batchDimA = util.sizeFromShape(outerDimsA);\n  const batchDimB = util.sizeFromShape(outerDimsB);\n\n  const outShapeOuterDims = broadcast_util.assertAndGetBroadcastShape(\n      a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n\n  util.assert(\n      innerShapeA === innerShapeB,\n      () => `Error in matMul: inner shapes (${innerShapeA}) and (` +\n          `${innerShapeB}) of Tensors with shapes ${a.shape} and ` +\n          `${b.shape} and transposeA=${transposeA}` +\n          ` and transposeB=${transposeB} must match.`);\n\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] :\n                                [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] :\n                                [batchDimB, innerShapeB, outerShapeB];\n\n  // The rest of the implementation is designed to operate on rank-3 tensors\n  const a3d = reshape({inputs: {x: a}, backend, attrs: {shape: a3dShape}});\n  const b3d = reshape({inputs: {x: b}, backend, attrs: {shape: b3dShape}});\n\n  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n  const batchDim = Math.max(batchDimA, batchDimB);\n\n  const a3dValues = backend.data.get(a3d.dataId).values as TypedArray;\n  const b3dValues = backend.data.get(b3d.dataId).values as TypedArray;\n\n  const a3dStrides = util.computeStrides(a3d.shape);\n  const b3dStrides = util.computeStrides(b3d.shape);\n\n  const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n      [a3dStrides[0], 1, a3dStrides[1]] :\n      [a3dStrides[0], a3dStrides[1], 1];\n  const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n      [1, b3dStrides[1], b3dStrides[0]] :\n      [b3dStrides[1], 1, b3dStrides[0]];\n\n  const size = leftDim * rightDim;\n  const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n\n  const resVals = result.values as TypedArray;\n  const blockSize = backend.blockSize;\n\n  for (let bi = 0; bi < batchDim; bi++) {\n    const batchIndexA = bi % batchDimA;\n    const batchIndexB = bi % batchDimB;\n    for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n      // for when blockSize doesn't evenly divide the input\n      const iBlock = Math.min(i0 + blockSize, leftDim);\n      for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n        const jBlock = Math.min(j0 + blockSize, rightDim);\n        for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n          const kBlock = Math.min(k0 + blockSize, sharedDim);\n\n          for (let i = i0; i < iBlock; i++) {\n            for (let j = j0; j < jBlock; j++) {\n              let sum = 0.0;\n\n              for (let k = k0; k < kBlock; k++) {\n                const aVal =\n                    // tslint:disable-next-line: max-line-length\n                    a3dValues[batchIndexA * aBatch + i * aOuterStep + k * aInnerStep];\n                const bVal =\n                    // tslint:disable-next-line: max-line-length\n                    b3dValues[k * bInnerStep + j * bOuterStep + batchIndexB * bBatch];\n                sum += aVal * bVal;\n              }\n              resVals[bi * size + (i * rightDim + j)] += sum;\n            }\n          }\n        }\n      }\n    }\n  }\n\n  backend.disposeIntermediateTensorInfo(a3d);\n  backend.disposeIntermediateTensorInfo(b3d);\n\n  // set correct shape on output.\n  return backend.makeTensorInfo(\n      outShape, result.dtype, result.values as TypedArray);\n}\n\nexport const batchMatMulConfig: KernelConfig = {\n  kernelName: BatchMatMul,\n  backendName: 'cpu',\n  kernelFunc: batchMatMul as unknown as KernelFunc,\n};\n"]}
|
