/**
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* @license
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* Copyright 2019 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 { ENGINE } from './engine';
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import * as tf from './index';
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import { ALL_ENVS, describeWithFlags } from './jasmine_util';
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import { expectArraysClose } from './test_util';
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describeWithFlags('gradients', ALL_ENVS, () => {
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it('matmul + relu', async () => {
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const a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
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const b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
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const [da, db] = tf.grads((a, b) => {
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// m = dot(a, b)
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// y = relu(m)
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// e = sum(y)
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const m = tf.matMul(a, b);
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const y = tf.relu(m);
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return tf.sum(y);
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})([a, b]);
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// de/dy = 1
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// dy/dm = step(m)
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// de/dm = de/dy * dy/dm = step(m)
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const dedm = tf.step(tf.matMul(a, b));
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// de/da = dot(de/dy, bT)
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expect(da.shape).toEqual(a.shape);
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let transposeA = false;
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let transposeB = true;
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expectArraysClose(await da.data(), await tf.matMul(dedm, b, transposeA, transposeB).data());
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// de/db = dot(aT, de/dy)
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expect(db.shape).toEqual(b.shape);
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transposeA = true;
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transposeB = false;
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expectArraysClose(await db.data(), await tf.matMul(a, dedm, transposeA, transposeB).data());
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});
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it('grad(f)', async () => {
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const grad = tf.grad(x => x.square());
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const result = grad(tf.tensor1d([.1, .2]));
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expectArraysClose(await result.data(), [.2, .4]);
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});
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it('calling grad(f) twice works', async () => {
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const grad = tf.grad(x => x.square());
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const result = grad(tf.tensor1d([.1, .2]));
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const result2 = grad(tf.tensor1d([.1, .4]));
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expectArraysClose(await result.data(), [.2, .4]);
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expectArraysClose(await result2.data(), [.2, .8]);
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});
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it('grad(f): throwing an error during forward pass', () => {
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const grad = tf.grad(x => {
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throw new Error('failed forward pass');
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});
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expect(() => grad(tf.zeros([]))).toThrowError();
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expect(ENGINE.isTapeOn()).toBe(false);
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});
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it('grad(f): throwing an error during backwards pass', () => {
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const customOp = tf.customGrad((x) => {
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return {
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value: x,
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gradFunc: () => {
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throw new Error('failed backward pass');
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}
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};
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});
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const grad = tf.grad(x => customOp(x));
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expect(() => grad(tf.zeros([]))).toThrowError();
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expect(ENGINE.isTapeOn()).toBe(false);
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});
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it('grads(f)', async () => {
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const grads = tf.grads(x => x.square());
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const result = grads([tf.tensor1d([.1, .2])]);
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expectArraysClose(await result[0].data(), [.2, .4]);
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});
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it('calling grads(f) twice works', async () => {
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const grads = tf.grads(x => x.square());
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const result = grads([tf.tensor1d([.1, .2])]);
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const result2 = grads([tf.tensor1d([.1, .4])]);
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expectArraysClose(await result[0].data(), [.2, .4]);
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expectArraysClose(await result2[0].data(), [.2, .8]);
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});
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it('works with reshape', async () => {
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const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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const exponent = tf.tensor1d([2, 2, 2, 2], 'int32');
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const da = tf.grad(a => {
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const b = a.flatten();
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const m = tf.pow(b, exponent);
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return tf.sum(m);
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})(a);
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expect(da.shape).toEqual([2, 2]);
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expectArraysClose(await da.data(), [2, 4, 6, 8]);
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});
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it('reshape outside tf.grads() throws error', () => {
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const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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const b = a.flatten();
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const exponent = tf.tensor1d([2, 2, 2, 2], 'int32');
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const f = () => {
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tf.grads((a, b) => {
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const m = tf.pow(b, exponent);
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return tf.sum(m);
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})([a, b]);
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};
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expect(f).toThrowError();
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});
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it('does not error if irrelevant (pruned) ops are missing grads', async () => {
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const a = tf.tensor1d([true, true], 'bool');
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const b = tf.tensor1d([false, true], 'bool');
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const da = tf.grad(a => {
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// Logical has no gradients, but it is irrelevant.
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a.logicalAnd(b);
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return a.sum();
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})(a);
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expectArraysClose(await da.data(), [1, 1]);
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});
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it('errors if relevant ops are missing grads', () => {
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const a = tf.tensor1d([true, true], 'bool');
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const b = tf.tensor1d([false, true], 'bool');
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const dfda = tf.grad(a => {
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// Logical has no gradients, but it's relevant to the output.
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return a.logicalAnd(b);
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});
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expect(() => dfda(a)).toThrowError();
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});
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it('works with asType', async () => {
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const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
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const exponent = tf.tensor2d([2, 2, 2, 2], [2, 2], 'int32');
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const da = tf.grad(a => {
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const b = a.toFloat();
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const m = tf.pow(b, exponent);
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return tf.sum(m);
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})(a);
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expect(da.shape).toEqual([2, 2]);
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expect(da.dtype).toEqual('float32');
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expectArraysClose(await da.data(), [2, 4, 6, 8]);
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});
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it('asType outside of tf.grads() throws error', () => {
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const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
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const b = a.toFloat();
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const exponent = tf.tensor2d([2, 2, 2, 2], [2, 2], 'int32');
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const f = () => {
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tf.grad(a => {
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const m = tf.pow(b, exponent);
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return tf.sum(m);
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})(a);
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};
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expect(f).toThrowError();
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});
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it('saves tensors from the forward pass as expected', () => {
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const x = tf.scalar(1).variable();
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const optimizer = tf.train.sgd(0.1);
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optimizer.minimize(() => {
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const y = x.square();
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const z = y.square();
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y.dispose();
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return z;
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});
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});
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it('custom ops do not leak', () => {
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const before = tf.memory().numTensors;
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const x = tf.softmax([1, 2, 3, 4]);
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x.dispose();
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const now = tf.memory().numTensors;
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expect(now).toBe(before);
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});
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});
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describeWithFlags('valueAndGradients', ALL_ENVS, () => {
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it('matmul + relu', async () => {
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const a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
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const b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
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const { value, grads } = tf.valueAndGrads((a, b) => {
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// m = dot(a, b)
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// y = relu(m)
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// e = sum(y)
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const m = tf.matMul(a, b);
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const y = tf.relu(m);
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return tf.sum(y);
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})([a, b]);
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expectArraysClose(await value.data(), 10);
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// de/dy = 1
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// dy/dm = step(m)
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// de/dm = de/dy * dy/dm = step(m)
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const dedm = tf.step(tf.matMul(a, b));
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const [da, db] = grads;
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// de/da = dot(de/dy, bT)
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let transposeA = false;
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let transposeB = true;
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expectArraysClose(await da.data(), await tf.matMul(dedm, b, transposeA, transposeB).data());
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// de/db = dot(aT, de/dy)
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transposeA = true;
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transposeB = false;
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expectArraysClose(await db.data(), await tf.matMul(a, dedm, transposeA, transposeB).data());
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});
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it('matmul + relu + inner tidy', async () => {
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const a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
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const b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
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const { value, grads } = tf.valueAndGrads((a, b) => {
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// m = dot(a, b)
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// y = relu(m)
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// e = sum(y)
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const m = tf.matMul(a, b);
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return tf.tidy(() => {
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const y = tf.relu(m);
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return tf.sum(y);
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});
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})([a, b]);
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expectArraysClose(await value.data(), 10);
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// de/dy = 1
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// dy/dm = step(m)
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// de/dm = de/dy * dy/dm = step(m)
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const dedm = tf.step(tf.matMul(a, b));
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const [da, db] = grads;
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// de/da = dot(de/dy, bT)
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let transposeA = false;
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let transposeB = true;
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expectArraysClose(await da.data(), await tf.matMul(dedm, b, transposeA, transposeB).data());
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// de/db = dot(aT, de/dy)
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transposeA = true;
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transposeB = false;
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expectArraysClose(await db.data(), await tf.matMul(a, dedm, transposeA, transposeB).data());
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});
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});
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describeWithFlags('higher-order gradients', ALL_ENVS, () => {
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it('grad(grad(f))', async () => {
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const x = tf.tensor1d([.1, .2]);
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const before = tf.memory().numTensors;
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const gradgrad = tf.grad(tf.grad(x => x.mul(x).mul(x)));
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const result = gradgrad(x);
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expect(tf.memory().numTensors).toBe(before + 1);
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expectArraysClose(await result.data(), [.6, 1.2]);
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});
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it('grad(grad(x^2))', async () => {
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const x = tf.scalar(3);
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const gradgrad = tf.grad(tf.grad(x => x.square()));
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const result = gradgrad(x);
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// grad(grad(x^2)) = grad(2x) = 2
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expectArraysClose(await result.data(), [2]);
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});
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it('grads(grads(f))', async () => {
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const grads = tf.grads(x => x.mul(x).mul(x));
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const gradsgrads = tf.grads(x => grads([x])[0]);
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const result = gradsgrads([tf.tensor1d([.1, .2])]);
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expectArraysClose(await result[0].data(), [.6, 1.2]);
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});
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});
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describeWithFlags('customGradient', ALL_ENVS, () => {
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it('basic', async () => {
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const a = tf.scalar(3);
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const b = tf.scalar(2, 'int32');
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const dy = tf.scalar(4);
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const customPow = tf.customGrad((a) => {
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const value = tf.pow(a, b);
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const gradFunc = (dy) => dy.mul(tf.scalar(0.1));
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return { value, gradFunc };
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});
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const { value, grad } = tf.valueAndGrad(a => customPow(a))(a, dy);
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expect(value.shape).toEqual(a.shape);
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expectArraysClose(await value.data(), [9]);
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expect(grad.shape).toEqual(a.shape);
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expectArraysClose(await grad.data(), [.4]);
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});
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it('second order derivative through customGradient', async () => {
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const a = tf.scalar(3);
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const b = tf.scalar(2, 'int32');
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const dy = tf.scalar(5);
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const customPow = tf.customGrad((a, save) => {
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const value = tf.pow(a, b);
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save([a]);
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const gradFunc = (dy, saved) => {
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const [a] = saved;
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return dy.mul(a);
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};
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return { value, gradFunc };
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});
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const dda = tf.grad(tf.grad(a => customPow(a)))(a, dy);
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expect(dda.shape).toEqual(a.shape);
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// First order: dy * a. Second order: dy.
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expectArraysClose(await dda.data(), await dy.data());
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});
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it('calling gradient of custom op twice works', async () => {
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const customOp = tf.customGrad((x, save) => {
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// Override gradient of our custom x ^ 2 op to be dy * abs(x);
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save([x]);
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return {
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value: x.square(),
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gradFunc: (dy, saved) => {
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const [x] = saved;
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return dy.mul(x.abs());
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}
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};
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});
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const x = tf.tensor1d([-1, -2, 3]);
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const grad = tf.grad(x => customOp(x));
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expectArraysClose(await grad(x).data(), [1, 2, 3]);
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expectArraysClose(await grad(x).data(), [1, 2, 3]);
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});
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});
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//# 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* @license\n * Copyright 2019 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 {ENGINE} from './engine';\nimport * as tf from './index';\nimport {ALL_ENVS, describeWithFlags} from './jasmine_util';\nimport {Tensor} from './tensor';\nimport {expectArraysClose} from './test_util';\n\ndescribeWithFlags('gradients', ALL_ENVS, () => {\n  it('matmul + relu', 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\n    const [da, db] = tf.grads((a: tf.Tensor2D, b: tf.Tensor2D) => {\n      // m = dot(a, b)\n      // y = relu(m)\n      // e = sum(y)\n      const m = tf.matMul(a, b);\n      const y = tf.relu(m);\n      return tf.sum(y);\n    })([a, b]);\n\n    // de/dy = 1\n    // dy/dm = step(m)\n    // de/dm = de/dy * dy/dm = step(m)\n    const dedm = tf.step(tf.matMul(a, b));\n\n    // de/da = dot(de/dy, bT)\n    expect(da.shape).toEqual(a.shape);\n    let transposeA = false;\n    let transposeB = true;\n    expectArraysClose(\n        await da.data(),\n        await tf.matMul(dedm, b, transposeA, transposeB).data());\n\n    // de/db = dot(aT, de/dy)\n    expect(db.shape).toEqual(b.shape);\n    transposeA = true;\n    transposeB = false;\n    expectArraysClose(\n        await db.data(),\n        await tf.matMul(a, dedm, transposeA, transposeB).data());\n  });\n\n  it('grad(f)', async () => {\n    const grad = tf.grad(x => x.square());\n    const result = grad(tf.tensor1d([.1, .2]));\n    expectArraysClose(await result.data(), [.2, .4]);\n  });\n\n  it('calling grad(f) twice works', async () => {\n    const grad = tf.grad(x => x.square());\n\n    const result = grad(tf.tensor1d([.1, .2]));\n    const result2 = grad(tf.tensor1d([.1, .4]));\n    expectArraysClose(await result.data(), [.2, .4]);\n    expectArraysClose(await result2.data(), [.2, .8]);\n  });\n\n  it('grad(f): throwing an error during forward pass', () => {\n    const grad = tf.grad(x => {\n      throw new Error('failed forward pass');\n    });\n    expect(() => grad(tf.zeros([]))).toThrowError();\n    expect(ENGINE.isTapeOn()).toBe(false);\n  });\n\n  it('grad(f): throwing an error during backwards pass', () => {\n    const customOp = tf.customGrad((x: tf.Tensor) => {\n      return {\n        value: x,\n        gradFunc: () => {\n          throw new Error('failed backward pass');\n        }\n      };\n    });\n    const grad = tf.grad(x => customOp(x));\n    expect(() => grad(tf.zeros([]))).toThrowError();\n    expect(ENGINE.isTapeOn()).toBe(false);\n  });\n\n  it('grads(f)', async () => {\n    const grads = tf.grads(x => x.square());\n    const result = grads([tf.tensor1d([.1, .2])]);\n    expectArraysClose(await result[0].data(), [.2, .4]);\n  });\n\n  it('calling grads(f) twice works', async () => {\n    const grads = tf.grads(x => x.square());\n\n    const result = grads([tf.tensor1d([.1, .2])]);\n    const result2 = grads([tf.tensor1d([.1, .4])]);\n    expectArraysClose(await result[0].data(), [.2, .4]);\n    expectArraysClose(await result2[0].data(), [.2, .8]);\n  });\n\n  it('works with reshape', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n    const exponent = tf.tensor1d([2, 2, 2, 2], 'int32');\n\n    const da = tf.grad(a => {\n      const b = a.flatten();\n      const m = tf.pow(b, exponent);\n      return tf.sum(m);\n    })(a);\n\n    expect(da.shape).toEqual([2, 2]);\n    expectArraysClose(await da.data(), [2, 4, 6, 8]);\n  });\n\n  it('reshape outside tf.grads() throws error', () => {\n    const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n    const b = a.flatten();\n    const exponent = tf.tensor1d([2, 2, 2, 2], 'int32');\n\n    const f = () => {\n      tf.grads((a, b) => {\n        const m = tf.pow(b, exponent);\n        return tf.sum(m);\n      })([a, b]);\n    };\n    expect(f).toThrowError();\n  });\n\n  it('does not error if irrelevant (pruned) ops are missing grads',\n     async () => {\n       const a = tf.tensor1d([true, true], 'bool');\n       const b = tf.tensor1d([false, true], 'bool');\n       const da = tf.grad(a => {\n         // Logical has no gradients, but it is irrelevant.\n         a.logicalAnd(b);\n         return a.sum();\n       })(a);\n       expectArraysClose(await da.data(), [1, 1]);\n     });\n\n  it('errors if relevant ops are missing grads', () => {\n    const a = tf.tensor1d([true, true], 'bool');\n    const b = tf.tensor1d([false, true], 'bool');\n    const dfda = tf.grad(a => {\n      // Logical has no gradients, but it's relevant to the output.\n      return a.logicalAnd(b);\n    });\n    expect(() => dfda(a)).toThrowError();\n  });\n\n  it('works with asType', async () => {\n    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');\n    const exponent = tf.tensor2d([2, 2, 2, 2], [2, 2], 'int32');\n\n    const da = tf.grad(a => {\n      const b = a.toFloat();\n      const m = tf.pow(b, exponent);\n      return tf.sum(m);\n    })(a);\n\n    expect(da.shape).toEqual([2, 2]);\n    expect(da.dtype).toEqual('float32');\n    expectArraysClose(await da.data(), [2, 4, 6, 8]);\n  });\n\n  it('asType outside of tf.grads() throws error', () => {\n    const a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');\n    const b = a.toFloat();\n    const exponent = tf.tensor2d([2, 2, 2, 2], [2, 2], 'int32');\n\n    const f = () => {\n      tf.grad(a => {\n        const m = tf.pow(b, exponent);\n        return tf.sum(m);\n      })(a);\n    };\n    expect(f).toThrowError();\n  });\n\n  it('saves tensors from the forward pass as expected', () => {\n    const x = tf.scalar(1).variable();\n    const optimizer = tf.train.sgd(0.1);\n    optimizer.minimize(() => {\n      const y = x.square();\n      const z = y.square();\n      y.dispose();\n      return z;\n    });\n  });\n\n  it('custom ops do not leak', () => {\n    const before = tf.memory().numTensors;\n    const x = tf.softmax([1, 2, 3, 4]);\n    x.dispose();\n    const now = tf.memory().numTensors;\n    expect(now).toBe(before);\n  });\n});\n\ndescribeWithFlags('valueAndGradients', ALL_ENVS, () => {\n  it('matmul + relu', 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\n    const {value, grads} =\n        tf.valueAndGrads((a: tf.Tensor2D, b: tf.Tensor2D) => {\n          // m = dot(a, b)\n          // y = relu(m)\n          // e = sum(y)\n          const m = tf.matMul(a, b);\n          const y = tf.relu(m);\n          return tf.sum(y);\n        })([a, b]);\n\n    expectArraysClose(await value.data(), 10);\n\n    // de/dy = 1\n    // dy/dm = step(m)\n    // de/dm = de/dy * dy/dm = step(m)\n    const dedm = tf.step(tf.matMul(a, b));\n\n    const [da, db] = grads;\n    // de/da = dot(de/dy, bT)\n    let transposeA = false;\n    let transposeB = true;\n    expectArraysClose(\n        await da.data(),\n        await tf.matMul(dedm, b, transposeA, transposeB).data());\n\n    // de/db = dot(aT, de/dy)\n    transposeA = true;\n    transposeB = false;\n    expectArraysClose(\n        await db.data(),\n        await tf.matMul(a, dedm, transposeA, transposeB).data());\n  });\n\n  it('matmul + relu + inner tidy', 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\n    const {value, grads} =\n        tf.valueAndGrads((a: tf.Tensor2D, b: tf.Tensor2D) => {\n          // m = dot(a, b)\n          // y = relu(m)\n          // e = sum(y)\n          const m = tf.matMul(a, b);\n          return tf.tidy(() => {\n            const y = tf.relu(m);\n            return tf.sum(y);\n          });\n        })([a, b]);\n\n    expectArraysClose(await value.data(), 10);\n\n    // de/dy = 1\n    // dy/dm = step(m)\n    // de/dm = de/dy * dy/dm = step(m)\n    const dedm = tf.step(tf.matMul(a, b));\n\n    const [da, db] = grads;\n    // de/da = dot(de/dy, bT)\n    let transposeA = false;\n    let transposeB = true;\n    expectArraysClose(\n        await da.data(),\n        await tf.matMul(dedm, b, transposeA, transposeB).data());\n\n    // de/db = dot(aT, de/dy)\n    transposeA = true;\n    transposeB = false;\n    expectArraysClose(\n        await db.data(),\n        await tf.matMul(a, dedm, transposeA, transposeB).data());\n  });\n});\n\ndescribeWithFlags('higher-order gradients', ALL_ENVS, () => {\n  it('grad(grad(f))', async () => {\n    const x = tf.tensor1d([.1, .2]);\n    const before = tf.memory().numTensors;\n    const gradgrad = tf.grad(tf.grad(x => x.mul(x).mul(x)));\n    const result = gradgrad(x);\n    expect(tf.memory().numTensors).toBe(before + 1);\n    expectArraysClose(await result.data(), [.6, 1.2]);\n  });\n\n  it('grad(grad(x^2))', async () => {\n    const x = tf.scalar(3);\n    const gradgrad = tf.grad(tf.grad(x => x.square()));\n    const result = gradgrad(x);\n    // grad(grad(x^2)) = grad(2x) = 2\n    expectArraysClose(await result.data(), [2]);\n  });\n\n  it('grads(grads(f))', async () => {\n    const grads = tf.grads(x => x.mul(x).mul(x));\n    const gradsgrads = tf.grads(x => grads([x])[0]);\n    const result = gradsgrads([tf.tensor1d([.1, .2])]);\n    expectArraysClose(await result[0].data(), [.6, 1.2]);\n  });\n});\n\ndescribeWithFlags('customGradient', ALL_ENVS, () => {\n  it('basic', async () => {\n    const a = tf.scalar(3);\n    const b = tf.scalar(2, 'int32');\n    const dy = tf.scalar(4);\n\n    const customPow = tf.customGrad((a: tf.Tensor) => {\n      const value = tf.pow(a, b);\n      const gradFunc = (dy: tf.Tensor) => dy.mul(tf.scalar(0.1));\n      return {value, gradFunc};\n    });\n\n    const {value, grad} = tf.valueAndGrad(a => customPow(a))(a, dy);\n    expect(value.shape).toEqual(a.shape);\n    expectArraysClose(await value.data(), [9]);\n    expect(grad.shape).toEqual(a.shape);\n    expectArraysClose(await grad.data(), [.4]);\n  });\n\n  it('second order derivative through customGradient', async () => {\n    const a = tf.scalar(3);\n    const b = tf.scalar(2, 'int32');\n\n    const dy = tf.scalar(5);\n\n    const customPow = tf.customGrad((a: tf.Tensor, save: tf.GradSaveFunc) => {\n      const value = tf.pow(a, b);\n      save([a]);\n      const gradFunc = (dy: tf.Tensor, saved: Tensor[]) => {\n        const [a] = saved;\n        return dy.mul(a);\n      };\n      return {value, gradFunc};\n    });\n\n    const dda = tf.grad(tf.grad(a => customPow(a)))(a, dy);\n    expect(dda.shape).toEqual(a.shape);\n\n    // First order: dy * a. Second order: dy.\n    expectArraysClose(await dda.data(), await dy.data());\n  });\n\n  it('calling gradient of custom op twice works', async () => {\n    const customOp = tf.customGrad((x: tf.Tensor, save: tf.GradSaveFunc) => {\n      // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n      save([x]);\n      return {\n        value: x.square(),\n        gradFunc: (dy, saved: Tensor[]) => {\n          const [x] = saved;\n          return dy.mul(x.abs());\n        }\n      };\n    });\n    const x = tf.tensor1d([-1, -2, 3]);\n    const grad = tf.grad(x => customOp(x));\n\n    expectArraysClose(await grad(x).data(), [1, 2, 3]);\n    expectArraysClose(await grad(x).data(), [1, 2, 3]);\n  });\n});\n"]}
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