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| /**
| * @license
| * Copyright 2020 Google LLC. All Rights Reserved.
| * Licensed under the Apache License, Version 2.0 (the "License");
| * you may not use this file except in compliance with the License.
| * You may obtain a copy of the License at
| *
| * http://www.apache.org/licenses/LICENSE-2.0
| *
| * Unless required by applicable law or agreed to in writing, software
| * distributed under the License is distributed on an "AS IS" BASIS,
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
| * See the License for the specific language governing permissions and
| * limitations under the License.
| * =============================================================================
| */
| import * as tf from '../../index';
| import { ALL_ENVS, describeWithFlags } from '../../jasmine_util';
| import { expectArraysClose } from '../../test_util';
| describeWithFlags('fused matmul', ALL_ENVS, () => {
| it('fused A x B', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const c = tf.fused.matMul({ a, b });
| expect(c.shape).toEqual([2, 2]);
| expectArraysClose(await c.data(), [0, 8, -3, 20]);
| });
| it('fused A x B with relu', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const transposeA = false;
| const transposeB = false;
| const c = tf.fused.matMul({ a, b, transposeA, transposeB, bias: null, activation: 'relu' });
| expect(c.shape).toEqual([2, 2]);
| expectArraysClose(await c.data(), [0, 8, 0, 20]);
| });
| it('fused A x B with elu', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const transposeA = false;
| const transposeB = false;
| const c = tf.fused.matMul({ a, b, transposeA, transposeB, bias: null, activation: 'elu' });
| expect(c.shape).toEqual([2, 2]);
| expectArraysClose(await c.data(), [0, 8, -0.9502, 20]);
| });
| it('fused A x B with relu6', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const transposeA = false;
| const transposeB = false;
| const c = tf.fused.matMul({ a, b, transposeA, transposeB, bias: null, activation: 'relu6' });
| expect(c.shape).toEqual([2, 2]);
| expectArraysClose(await c.data(), [0, 6, 0, 6]);
| });
| it('fused A x B with prelu', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const alpha = tf.tensor2d([0.5, 0.5], [1, 2]);
| const transposeA = false;
| const transposeB = false;
| const c = tf.fused.matMul({
| a,
| b,
| transposeA,
| transposeB,
| bias: null,
| activation: 'prelu',
| preluActivationWeights: alpha
| });
| expect(c.shape).toEqual([2, 2]);
| expectArraysClose(await c.data(), [0, 8, -1.5, 20]);
| });
| it('fused A x B with leakyrelu', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const alpha = 0.3;
| const transposeA = false;
| const transposeB = false;
| const c = tf.fused.matMul({
| a,
| b,
| transposeA,
| transposeB,
| bias: null,
| activation: 'leakyrelu',
| leakyreluAlpha: alpha
| });
| expect(c.shape).toEqual([2, 2]);
| expectArraysClose(await c.data(), [0, 8, -0.9000000357627869, 20]);
| });
| it('fused A x B with leakyrelu not provided.', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const transposeA = false;
| const transposeB = false;
| const c = tf.fused.matMul({
| a,
| b,
| transposeA,
| transposeB,
| bias: null,
| activation: 'leakyrelu'
| });
| expect(c.shape).toEqual([2, 2]);
| // leakyRelu should use default alpha=0.2.
| expectArraysClose(await c.data(), [0, 8, -0.6000000238418579, 20]);
| });
| it('fused A x B with sigmoid', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const transposeA = false;
| const transposeB = false;
| const c = tf.fused.matMul({ a, b, transposeA, transposeB, bias: null, activation: 'sigmoid' });
| expect(c.shape).toEqual([2, 2]);
| expectArraysClose(await c.data(), [0.5, 0.99966455, 0.04742587, 1]);
| });
| it('fused A x B with relu transpose', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [2, 3]);
| const transposeA = false;
| const transposeB = true;
| const c = tf.fused.matMul({ a, b, transposeA, transposeB, bias: null, activation: 'relu' });
| expect(c.shape).toEqual([2, 2]);
| expectArraysClose(await c.data(), [0, 9, 0, 24]);
| });
| it('fused A x B with 2d bias and relu', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
| const transposeA = false;
| const transposeB = false;
| const d = tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: 'relu' });
| expect(d.shape).toEqual([2, 2]);
| expectArraysClose(await d.data(), [1, 9, 0, 21]);
| });
| it('fused A x B with relu and broadcasted bias', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const c = tf.tensor1d([1, 1]);
| const act = 'relu';
| const transposeA = false;
| const transposeB = false;
| const d = tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: act });
| expect(d.shape).toEqual([2, 2]);
| expectArraysClose(await d.data(), [1, 9, 0, 21]);
| });
| it('fused A x B with elu and broadcasted bias', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const c = tf.tensor1d([1, 1]);
| const act = 'elu';
| const transposeA = false;
| const transposeB = false;
| const d = tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: act });
| expect(d.shape).toEqual([2, 2]);
| expectArraysClose(await d.data(), [1, 9, -0.8647, 21]);
| });
| it('fused A x B with elu and broadcasted shape', async () => {
| const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const c = tf.tensor1d([1, 1]);
| const act = 'elu';
| const transposeA = false;
| const transposeB = false;
| const d = tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: act });
| expect(d.shape).toEqual([1, 2, 2]);
| expectArraysClose(await d.data(), [1, 9, -0.8647, 21]);
| });
| it('fused A x B with relu and broadcasted bias different rank', async () => {
| const a = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [2, 2, 3]);
| const b = tf.tensor3d([0, 1, -3, 2, 2, 1, 0, 1, -3, 2, 2, 1], [2, 3, 2]);
| const c = tf.tensor2d([1, 2], [1, 2]);
| const act = 'relu';
| const transposeA = false;
| const transposeB = false;
| const d = tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: act });
| expect(d.shape).toEqual([2, 2, 2]);
| expectArraysClose(await d.data(), [2, 6, 0, 18, 0, 30, 0, 42]);
| });
| it('fused A x B with 2d bias only', async () => {
| const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
| const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
| const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
| const transposeA = false;
| const transposeB = false;
| const d = tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: 'linear' });
| expect(d.shape).toEqual([2, 2]);
| expectArraysClose(await d.data(), [1, 9, -2, 21]);
| });
| it('fused A x B with relu gradient', async () => {
| const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
| const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
| const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
| const transposeA = false;
| const transposeB = false;
| const grads = tf.grads((a, b) => {
| const prod = tf.matMul(a, b, transposeA, transposeB);
| return tf.relu(prod);
| });
| const fusedGrads = tf.grads((a, b) => {
| return tf.fused.matMul({ a, b, transposeA, transposeB, bias: null, activation: 'relu' });
| });
| const [da, db] = grads([a, b], dy);
| const [fusedDa, fusedDb] = fusedGrads([a, b], dy);
| expectArraysClose(await da.array(), await fusedDa.array());
| expectArraysClose(await db.data(), await fusedDb.array());
| });
| it('gradient with clones A x B with relu', () => {
| const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
| const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
| const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
| const transposeA = false;
| const transposeB = false;
| const fusedGrads = tf.grads((a, b) => {
| return tf.fused
| .matMul({
| a: a.clone(),
| b: b.clone(),
| transposeA,
| transposeB,
| bias: null,
| activation: 'relu'
| })
| .clone();
| });
| const [fusedDa, fusedDb] = fusedGrads([a, b], dy);
| expect(fusedDa.shape).toEqual(a.shape);
| expect(fusedDb.shape).toEqual(b.shape);
| });
| it('fused A x B with relu bias gradient', async () => {
| const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
| const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
| const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
| const transposeA = false;
| const transposeB = false;
| const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
| const grads = tf.grads((a, b, c) => {
| const prod = tf.matMul(a, b, transposeA, transposeB);
| const sum = tf.add(prod, c);
| return tf.relu(sum);
| });
| const fusedGrads = tf.grads((a, b, c) => {
| return tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: 'relu' });
| });
| const [da, db, dc] = grads([a, b, c], dy);
| const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);
| expectArraysClose(await da.array(), await fusedDa.array());
| expectArraysClose(await db.array(), await fusedDb.array());
| expectArraysClose(await dc.array(), await fusedDc.array());
| });
| it('fused A x B with relu bias gradient transpose', async () => {
| const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [3, 2]);
| const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
| const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
| const transposeA = true;
| const transposeB = false;
| const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
| const grads = tf.grads((a, b, c) => {
| const prod = tf.matMul(a, b, transposeA, transposeB);
| const sum = tf.add(prod, c);
| return tf.relu(sum);
| });
| const fusedGrads = tf.grads((a, b, c) => {
| return tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: 'relu' });
| });
| const [da, db, dc] = grads([a, b, c], dy);
| const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);
| expectArraysClose(await da.array(), await fusedDa.array());
| expectArraysClose(await db.array(), await fusedDb.array());
| expectArraysClose(await dc.array(), await fusedDc.array());
| });
| it('fused A x B with relu and broadcasted bias gradient', async () => {
| const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
| const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
| const c = tf.tensor2d([[1]]);
| const transposeA = false;
| const transposeB = false;
| const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
| const grads = tf.grads((a, b, c) => {
| const prod = tf.matMul(a, b, transposeA, transposeB);
| const sum = tf.add(prod, c);
| return tf.relu(sum);
| });
| const fusedGrads = tf.grads((a, b, c) => {
| return tf.fused.matMul({ a, b, transposeA, transposeB, bias: c, activation: 'relu' });
| });
| const [da, db, dc] = grads([a, b, c], dy);
| const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);
| expectArraysClose(await da.array(), await fusedDa.array());
| expectArraysClose(await db.array(), await fusedDb.array());
| expectArraysClose(await dc.array(), await fusedDc.array());
| });
| it('fused matmul with relu6 and gradients', async () => {
| const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
| const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
| const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
| const transposeA = false;
| const transposeB = false;
| const fusedGrads = tf.grads((a, b) => {
| return tf.fused.matMul({ a, b, transposeA, transposeB, bias: null, activation: 'relu6' });
| });
| const [fusedDa, fusedDb] = fusedGrads([a, b], dy);
| const grads = tf.grads((a, b) => {
| const prod = tf.matMul(a, b, transposeA, transposeB);
| return tf.relu6(prod);
| });
| const [da, db] = grads([a, b], dy);
| expectArraysClose(await da.array(), await fusedDa.array());
| expectArraysClose(await db.data(), await fusedDb.array());
| });
| });
| //# 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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 * as tf from '../../index';\nimport {ALL_ENVS, describeWithFlags} from '../../jasmine_util';\nimport {expectArraysClose} from '../../test_util';\n\ndescribeWithFlags('fused matmul', ALL_ENVS, () => {\n  it('fused A x B', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n\n    const c = tf.fused.matMul({a, b});\n\n    expect(c.shape).toEqual([2, 2]);\n    expectArraysClose(await c.data(), [0, 8, -3, 20]);\n  });\n\n  it('fused A x B with relu', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const c = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: null, activation: 'relu'});\n\n    expect(c.shape).toEqual([2, 2]);\n    expectArraysClose(await c.data(), [0, 8, 0, 20]);\n  });\n\n  it('fused A x B with elu', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const c = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: null, activation: 'elu'});\n\n    expect(c.shape).toEqual([2, 2]);\n    expectArraysClose(await c.data(), [0, 8, -0.9502, 20]);\n  });\n\n  it('fused A x B with relu6', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const c = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: null, activation: 'relu6'});\n\n    expect(c.shape).toEqual([2, 2]);\n    expectArraysClose(await c.data(), [0, 6, 0, 6]);\n  });\n\n  it('fused A x B with prelu', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const alpha = tf.tensor2d([0.5, 0.5], [1, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const c = tf.fused.matMul({\n      a,\n      b,\n      transposeA,\n      transposeB,\n      bias: null,\n      activation: 'prelu',\n      preluActivationWeights: alpha\n    });\n\n    expect(c.shape).toEqual([2, 2]);\n    expectArraysClose(await c.data(), [0, 8, -1.5, 20]);\n  });\n\n  it('fused A x B with leakyrelu', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const alpha = 0.3;\n    const transposeA = false;\n    const transposeB = false;\n\n    const c = tf.fused.matMul({\n      a,\n      b,\n      transposeA,\n      transposeB,\n      bias: null,\n      activation: 'leakyrelu',\n      leakyreluAlpha: alpha\n    });\n\n    expect(c.shape).toEqual([2, 2]);\n    expectArraysClose(await c.data(), [0, 8, -0.9000000357627869, 20]);\n  });\n\n  it('fused A x B with leakyrelu not provided.', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const c = tf.fused.matMul({\n      a,\n      b,\n      transposeA,\n      transposeB,\n      bias: null,\n      activation: 'leakyrelu'\n    });\n\n    expect(c.shape).toEqual([2, 2]);\n    // leakyRelu should use default alpha=0.2.\n    expectArraysClose(await c.data(), [0, 8, -0.6000000238418579, 20]);\n  });\n\n  it('fused A x B with sigmoid', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const c = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: null, activation: 'sigmoid'});\n\n    expect(c.shape).toEqual([2, 2]);\n    expectArraysClose(await c.data(), [0.5, 0.99966455, 0.04742587, 1]);\n  });\n\n  it('fused A x B with relu transpose', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [2, 3]);\n    const transposeA = false;\n    const transposeB = true;\n\n    const c = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: null, activation: 'relu'});\n\n    expect(c.shape).toEqual([2, 2]);\n    expectArraysClose(await c.data(), [0, 9, 0, 24]);\n  });\n\n  it('fused A x B with 2d bias and relu', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const d = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: c, activation: 'relu'});\n\n    expect(d.shape).toEqual([2, 2]);\n    expectArraysClose(await d.data(), [1, 9, 0, 21]);\n  });\n\n  it('fused A x B with relu and broadcasted bias', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const c = tf.tensor1d([1, 1]);\n    const act: tf.fused.Activation = 'relu';\n    const transposeA = false;\n    const transposeB = false;\n\n    const d = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: c, activation: act});\n\n    expect(d.shape).toEqual([2, 2]);\n    expectArraysClose(await d.data(), [1, 9, 0, 21]);\n  });\n\n  it('fused A x B with elu and broadcasted bias', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const c = tf.tensor1d([1, 1]);\n    const act: tf.fused.Activation = 'elu';\n    const transposeA = false;\n    const transposeB = false;\n\n    const d = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: c, activation: act});\n\n    expect(d.shape).toEqual([2, 2]);\n    expectArraysClose(await d.data(), [1, 9, -0.8647, 21]);\n  });\n\n  it('fused A x B with elu and broadcasted shape', async () => {\n    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [1, 2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const c = tf.tensor1d([1, 1]);\n    const act: tf.fused.Activation = 'elu';\n    const transposeA = false;\n    const transposeB = false;\n\n    const d = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: c, activation: act});\n\n    expect(d.shape).toEqual([1, 2, 2]);\n    expectArraysClose(await d.data(), [1, 9, -0.8647, 21]);\n  });\n\n  it('fused A x B with relu and broadcasted bias different rank', async () => {\n    const a = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [2, 2, 3]);\n    const b = tf.tensor3d([0, 1, -3, 2, 2, 1, 0, 1, -3, 2, 2, 1], [2, 3, 2]);\n    const c = tf.tensor2d([1, 2], [1, 2]);\n    const act: tf.fused.Activation = 'relu';\n    const transposeA = false;\n    const transposeB = false;\n\n    const d = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: c, activation: act});\n\n    expect(d.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await d.data(), [2, 6, 0, 18, 0, 30, 0, 42]);\n  });\n\n  it('fused A x B with 2d bias only', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n    const b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);\n    const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const d = tf.fused.matMul(\n        {a, b, transposeA, transposeB, bias: c, activation: 'linear'});\n\n    expect(d.shape).toEqual([2, 2]);\n    expectArraysClose(await d.data(), [1, 9, -2, 21]);\n  });\n\n  it('fused A x B with relu gradient', async () => {\n    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);\n    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);\n    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const grads = tf.grads((a, b) => {\n      const prod = tf.matMul(a, b, transposeA, transposeB);\n      return tf.relu(prod);\n    });\n\n    const fusedGrads = tf.grads((a, b) => {\n      return tf.fused.matMul(\n          {a, b, transposeA, transposeB, bias: null, activation: 'relu'});\n    });\n\n    const [da, db] = grads([a, b], dy);\n    const [fusedDa, fusedDb] = fusedGrads([a, b], dy);\n    expectArraysClose(await da.array(), await fusedDa.array());\n    expectArraysClose(await db.data(), await fusedDb.array());\n  });\n\n  it('gradient with clones A x B with relu', () => {\n    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);\n    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);\n    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const fusedGrads = tf.grads((a, b) => {\n      return tf.fused\n          .matMul({\n            a: a.clone(),\n            b: b.clone(),\n            transposeA,\n            transposeB,\n            bias: null,\n            activation: 'relu'\n          })\n          .clone();\n    });\n\n    const [fusedDa, fusedDb] = fusedGrads([a, b], dy);\n    expect(fusedDa.shape).toEqual(a.shape);\n    expect(fusedDb.shape).toEqual(b.shape);\n  });\n\n  it('fused A x B with relu bias gradient', async () => {\n    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);\n    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);\n    const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);\n\n    const grads = tf.grads((a, b, c) => {\n      const prod = tf.matMul(a, b, transposeA, transposeB);\n      const sum = tf.add(prod, c);\n      return tf.relu(sum);\n    });\n\n    const fusedGrads = tf.grads((a, b, c) => {\n      return tf.fused.matMul(\n          {a, b, transposeA, transposeB, bias: c, activation: 'relu'});\n    });\n\n    const [da, db, dc] = grads([a, b, c], dy);\n    const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);\n\n    expectArraysClose(await da.array(), await fusedDa.array());\n    expectArraysClose(await db.array(), await fusedDb.array());\n    expectArraysClose(await dc.array(), await fusedDc.array());\n  });\n\n  it('fused A x B with relu bias gradient transpose', async () => {\n    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [3, 2]);\n    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);\n    const c = tf.tensor2d([1, 1, 1, 1], [2, 2]);\n    const transposeA = true;\n    const transposeB = false;\n\n    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);\n\n    const grads = tf.grads((a, b, c) => {\n      const prod = tf.matMul(a, b, transposeA, transposeB);\n      const sum = tf.add(prod, c);\n      return tf.relu(sum);\n    });\n\n    const fusedGrads = tf.grads((a, b, c) => {\n      return tf.fused.matMul(\n          {a, b, transposeA, transposeB, bias: c, activation: 'relu'});\n    });\n\n    const [da, db, dc] = grads([a, b, c], dy);\n    const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);\n\n    expectArraysClose(await da.array(), await fusedDa.array());\n    expectArraysClose(await db.array(), await fusedDb.array());\n    expectArraysClose(await dc.array(), await fusedDc.array());\n  });\n\n  it('fused A x B with relu and broadcasted bias gradient', async () => {\n    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);\n    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);\n    const c = tf.tensor2d([[1]]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);\n\n    const grads = tf.grads((a, b, c) => {\n      const prod = tf.matMul(a, b, transposeA, transposeB);\n      const sum = tf.add(prod, c);\n      return tf.relu(sum);\n    });\n\n    const fusedGrads = tf.grads((a, b, c) => {\n      return tf.fused.matMul(\n          {a, b, transposeA, transposeB, bias: c, activation: 'relu'});\n    });\n\n    const [da, db, dc] = grads([a, b, c], dy);\n    const [fusedDa, fusedDb, fusedDc] = fusedGrads([a, b, c], dy);\n\n    expectArraysClose(await da.array(), await fusedDa.array());\n    expectArraysClose(await db.array(), await fusedDb.array());\n    expectArraysClose(await dc.array(), await fusedDc.array());\n  });\n\n  it('fused matmul with relu6 and gradients', async () => {\n    const a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);\n    const b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);\n    const dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);\n    const transposeA = false;\n    const transposeB = false;\n\n    const fusedGrads = tf.grads((a, b) => {\n      return tf.fused.matMul(\n          {a, b, transposeA, transposeB, bias: null, activation: 'relu6'});\n    });\n    const [fusedDa, fusedDb] = fusedGrads([a, b], dy);\n\n    const grads = tf.grads((a, b) => {\n      const prod = tf.matMul(a, b, transposeA, transposeB);\n      return tf.relu6(prod);\n    });\n    const [da, db] = grads([a, b], dy);\n\n    expectArraysClose(await da.array(), await fusedDa.array());\n    expectArraysClose(await db.data(), await fusedDb.array());\n  });\n});\n"]}
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