From c0eb14d67c02649069b76e30b58c202eee8302b5 Mon Sep 17 00:00:00 2001
From: Na Li <linazhao@google.com>
Date: Tue, 31 Mar 2020 10:28:29 -0700
Subject: [PATCH 1/7] [core]Modularize add.

---
 tfjs-core/src/gradients/Add_grad.ts           | 48 ++++++++++++++
 tfjs-core/src/kernel_names.ts                 | 13 ++--
 tfjs-core/src/ops/add.ts                      | 65 +++++++++++++++++++
 tfjs-core/src/ops/binary_ops.ts               | 56 ----------------
 tfjs-core/src/ops/ops.ts                      | 11 ++--
 tfjs-core/src/public/chained_ops/add.ts       | 29 +++++++++
 .../chained_ops/register_all_chained_ops.ts   |  2 +-
 .../register_all_chained_ops_test.ts          |  5 ++
 tfjs-core/src/register_all_gradients.ts       |  3 +-
 tfjs-core/src/tensor.ts                       |  6 --
 10 files changed, 164 insertions(+), 74 deletions(-)
 create mode 100644 tfjs-core/src/gradients/Add_grad.ts
 create mode 100644 tfjs-core/src/ops/add.ts
 create mode 100644 tfjs-core/src/public/chained_ops/add.ts

diff --git a/tfjs-core/src/gradients/Add_grad.ts b/tfjs-core/src/gradients/Add_grad.ts
new file mode 100644
index 00000000000..5d2bd450578
--- /dev/null
+++ b/tfjs-core/src/gradients/Add_grad.ts
@@ -0,0 +1,48 @@
+import {Add} from '../kernel_names';
+import {GradConfig} from '../kernel_registry';
+/**
+ * @license
+ * Copyright 2020 Google Inc. 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 broadcast_util from '../ops/broadcast_util';
+import {Tensor} from '../tensor';
+
+export const addGradConfig: GradConfig = {
+  kernelName: Add,
+  gradFunc: (dy: Tensor, saved: Tensor[]) => {
+    const [a, b] = saved;
+    const outShape =
+        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);
+
+    const derA = () => {
+      let res = dy;
+      const reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = res.sum(reduceAxes);
+      }
+      return res.reshape(a.shape);
+    };
+    const derB = () => {
+      let res = dy;
+      const reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = res.sum(reduceAxes);
+      }
+      return res.reshape(b.shape);
+    };
+
+    return {a: derA, b: derB};
+  }
+}
diff --git a/tfjs-core/src/kernel_names.ts b/tfjs-core/src/kernel_names.ts
index 78d85fa6927..3d9ce3b440c 100644
--- a/tfjs-core/src/kernel_names.ts
+++ b/tfjs-core/src/kernel_names.ts
@@ -21,6 +21,14 @@
 import {NamedTensorInfoMap} from './kernel_registry';
 import {PixelData} from './types';
 
+export const Add = 'Add';
+export type AddInputs = BinaryInputs;
+
+export type BinaryInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;
+
+export const Div = 'Div';
+export type DivInputs = BinaryInputs;
+
 export const FusedBatchNorm = 'FusedBatchNorm';
 export type FusedBatchNormInputs =
     Pick<NamedTensorInfoMap, 'x'|'scale'|'offset'|'mean'|'variance'>;
@@ -28,11 +36,6 @@ export interface FusedBatchNormAttrs {
   varianceEpsilon: number;
 }
 
-export type BinaryInputs = Pick<NamedTensorInfoMap, 'a'|'b'>;
-
-export const Div = 'Div';
-export type DivInputs = BinaryInputs;
-
 export const SquaredDifference = 'SquaredDifference';
 export type SquaredDifferenceInputs = BinaryInputs;
 
diff --git a/tfjs-core/src/ops/add.ts b/tfjs-core/src/ops/add.ts
new file mode 100644
index 00000000000..d7fb8c41d63
--- /dev/null
+++ b/tfjs-core/src/ops/add.ts
@@ -0,0 +1,65 @@
+/**
+ * @license
+ * Copyright 2020 Google Inc. 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 {ENGINE, ForwardFunc} from '../engine';
+import {Add, AddInputs} from '../kernel_names';
+import {Tensor} from '../tensor';
+import {NamedTensorMap} from '../tensor_types';
+import {makeTypesMatch} from '../tensor_util';
+import {convertToTensor} from '../tensor_util_env';
+import {TensorLike} from '../types';
+
+import {op} from './operation';
+
+/**
+ * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
+ *
+ * We also expose `tf.addStrict` which has the same signature as this op and
+ * asserts that `a` and `b` are the same shape (does not broadcast).
+ *
+ * ```js
+ * const a = tf.tensor1d([1, 2, 3, 4]);
+ * const b = tf.tensor1d([10, 20, 30, 40]);
+ *
+ * a.add(b).print();  // or tf.add(a, b)
+ * ```
+ *
+ * ```js
+ * // Broadcast add a with b.
+ * const a = tf.scalar(5);
+ * const b = tf.tensor1d([10, 20, 30, 40]);
+ *
+ * a.add(b).print();  // or tf.add(a, b)
+ * ```
+ * @param a The first `tf.Tensor` to add.
+ * @param b The second `tf.Tensor` to add. Must have the same type as `a`.
+ */
+/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
+function add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
+  let $a = convertToTensor(a, 'a', 'add');
+  let $b = convertToTensor(b, 'b', 'add');
+  [$a, $b] = makeTypesMatch($a, $b);
+
+  const forward: ForwardFunc<Tensor> = backend => backend.add($a, $b);
+
+  const inputs: AddInputs = {a: $a, b: $b};
+
+  return ENGINE.runKernelFunc(
+             forward, inputs as {} as NamedTensorMap, null /* gradient */,
+             Add) as T;
+}
+
+export const add = op({add_});
diff --git a/tfjs-core/src/ops/binary_ops.ts b/tfjs-core/src/ops/binary_ops.ts
index ac5e0525ce4..60a95401028 100644
--- a/tfjs-core/src/ops/binary_ops.ts
+++ b/tfjs-core/src/ops/binary_ops.ts
@@ -27,61 +27,6 @@ import {op} from './operation';
 import {scalar, zerosLike} from './tensor_ops';
 import {neg} from './unary_ops';
 
-/**
- * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
- *
- * We also expose `tf.addStrict` which has the same signature as this op and
- * asserts that `a` and `b` are the same shape (does not broadcast).
- *
- * ```js
- * const a = tf.tensor1d([1, 2, 3, 4]);
- * const b = tf.tensor1d([10, 20, 30, 40]);
- *
- * a.add(b).print();  // or tf.add(a, b)
- * ```
- *
- * ```js
- * // Broadcast add a with b.
- * const a = tf.scalar(5);
- * const b = tf.tensor1d([10, 20, 30, 40]);
- *
- * a.add(b).print();  // or tf.add(a, b)
- * ```
- * @param a The first `tf.Tensor` to add.
- * @param b The second `tf.Tensor` to add. Must have the same type as `a`.
- */
-/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
-function add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
-  let $a = convertToTensor(a, 'a', 'add');
-  let $b = convertToTensor(b, 'b', 'add');
-  [$a, $b] = makeTypesMatch($a, $b);
-
-  const outShape =
-      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
-
-  const der = (dy: Tensor) => {
-    const derA = () => {
-      let res = dy;
-      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = res.sum(reduceAxes);
-      }
-      return res.reshape($a.shape);
-    };
-    const derB = () => {
-      let res = dy;
-      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
-      if (reduceAxes.length > 0) {
-        res = res.sum(reduceAxes);
-      }
-      return res.reshape($b.shape);
-    };
-    return {a: derA, b: derB};
-  };
-  return ENGINE.runKernelFunc(
-             backend => backend.add($a, $b), {a: $a, b: $b}, der, 'Add') as T;
-}
-
 /**
  * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
  *
@@ -728,7 +673,6 @@ function atan2_<T extends Tensor>(
   }, {$a, $b}, der) as T;
 }
 
-export const add = op({add_});
 export const addN = op({addN_});
 export const addStrict = op({addStrict_});
 export const atan2 = op({atan2_});
diff --git a/tfjs-core/src/ops/ops.ts b/tfjs-core/src/ops/ops.ts
index 0f45e6e42bf..32dcfac224d 100644
--- a/tfjs-core/src/ops/ops.ts
+++ b/tfjs-core/src/ops/ops.ts
@@ -1,6 +1,6 @@
 /**
  * @license
- * Copyright 2018 Google Inc. All Rights Reserved.
+ * Copyright 2020 Google Inc. 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
@@ -16,10 +16,11 @@
  */
 
 // Modularized ops.
-export {batchNormalization, batchNorm} from './batchnorm';
-export {batchNormalization2d, batchNorm2d} from './batchnorm2d';
-export {batchNormalization3d, batchNorm3d} from './batchnorm3d';
-export {batchNormalization4d, batchNorm4d} from './batchnorm4d';
+export {add} from './add';
+export {batchNorm, batchNormalization} from './batchnorm';
+export {batchNorm2d, batchNormalization2d} from './batchnorm2d';
+export {batchNorm3d, batchNormalization3d} from './batchnorm3d';
+export {batchNorm4d, batchNormalization4d} from './batchnorm4d';
 export {broadcastTo} from './broadcast_to';
 export {clone} from './clone';
 export {div} from './div';
diff --git a/tfjs-core/src/public/chained_ops/add.ts b/tfjs-core/src/public/chained_ops/add.ts
new file mode 100644
index 00000000000..83ca43ed49b
--- /dev/null
+++ b/tfjs-core/src/public/chained_ops/add.ts
@@ -0,0 +1,29 @@
+/**
+ * @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 {add} from '../../ops/add';
+import {Tensor} from '../../tensor';
+import {Rank, TensorLike} from '../../types';
+
+declare module '../../tensor' {
+  interface Tensor<R extends Rank = Rank> {
+    add<T extends Tensor>(b: Tensor|TensorLike): T;
+  }
+}
+
+Tensor.prototype.add = function<T extends Tensor>(b: Tensor|TensorLike): T {
+  return add(this, b);
+};
diff --git a/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts b/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts
index ca8aa8194db..0f969b622fa 100644
--- a/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts
+++ b/tfjs-core/src/public/chained_ops/register_all_chained_ops.ts
@@ -14,7 +14,7 @@
  * limitations under the License.
  * =============================================================================
  */
-
+import './add';
 import './broadcast_to';
 import './div';
 import './div_no_nan';
diff --git a/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts b/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts
index 8f1630653d2..b82818be73c 100644
--- a/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts
+++ b/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts
@@ -24,8 +24,13 @@ import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
 // flexibility to change in future.
 
 const CHAINED_OPS = [
+<<<<<<< HEAD
   'square', 'broadcastTo', 'tile', 'oneHot', 'div', 'divNoNan', 'transpose',
   'pad', 'batchNorm'
+=======
+  'add', 'broadcastTo', 'div', 'divNoNan', 'oneHot', 'pad', 'square', 'tile',
+  'transpose'
+>>>>>>> [core]Modularize add.
 ];
 
 describeWithFlags('chained ops', ALL_ENVS, () => {
diff --git a/tfjs-core/src/register_all_gradients.ts b/tfjs-core/src/register_all_gradients.ts
index 35974b826ac..62d0c1b738a 100644
--- a/tfjs-core/src/register_all_gradients.ts
+++ b/tfjs-core/src/register_all_gradients.ts
@@ -14,6 +14,7 @@
  * limitations under the License.
  * =============================================================================
  */
+import {addGradConfig} from './gradients/Add_grad';
 import {broadcastToGradConfig} from './gradients/BroadcastTo_grad';
 import {divGradConfig} from './gradients/Div_grad';
 import {fusedBatchNormGradConfig} from './gradients/FusedBatchNorm_grad';
@@ -29,7 +30,7 @@ import {registerGradient} from './kernel_registry';
 
 // Export all kernel configs here so that the package can auto register them
 const gradConfigs: GradConfig[] = [
-  broadcastToGradConfig, divGradConfig, fusedBatchNormGradConfig,
+  addGradConfig, broadcastToGradConfig, divGradConfig, fusedBatchNormGradConfig,
   identityGradConfig, oneHotGradConfig, padV2GradConfig, squareGradConfig,
   squaredDifferenceGradConfig, tileGradConfig, transposeGradConfig
 ];
diff --git a/tfjs-core/src/tensor.ts b/tfjs-core/src/tensor.ts
index 83345f02555..d8cc002fca6 100644
--- a/tfjs-core/src/tensor.ts
+++ b/tfjs-core/src/tensor.ts
@@ -208,7 +208,6 @@ export interface OpHandler {
   max<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;
   argMin<T extends Tensor>(x: Tensor, axis: number): T;
   argMax<T extends Tensor>(x: Tensor, axis: number): T;
-  add<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;
   addStrict<T extends Tensor>(a: T, b: T|TensorLike): T;
   atan2<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;
   sub<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;
@@ -877,11 +876,6 @@ export class Tensor<R extends Rank = Rank> {
   }
 
   // Binary ops.
-
-  add<T extends Tensor>(x: Tensor|TensorLike): T {
-    this.throwIfDisposed();
-    return opHandler.add(this, x);
-  }
   addStrict<T extends this>(this: T, x: T|TensorLike): T {
     this.throwIfDisposed();
     return opHandler.addStrict(this, x);

From 7c51c89e240ba1a5528cdf3dd9bf91379ca9ff34 Mon Sep 17 00:00:00 2001
From: Na Li <linazhao@google.com>
Date: Tue, 31 Mar 2020 10:56:04 -0700
Subject: [PATCH 2/7] .

---
 tfjs-core/src/ops/binary_ops.ts | 3 +++
 tfjs-core/src/ops/fused_ops.ts  | 3 +--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/tfjs-core/src/ops/binary_ops.ts b/tfjs-core/src/ops/binary_ops.ts
index 60a95401028..a9455c24b8d 100644
--- a/tfjs-core/src/ops/binary_ops.ts
+++ b/tfjs-core/src/ops/binary_ops.ts
@@ -22,11 +22,14 @@ import {makeTypesMatch} from '../tensor_util';
 import {convertToTensor} from '../tensor_util_env';
 import {TensorLike} from '../types';
 import * as util from '../util';
+
+import {add} from './add';
 import * as broadcast_util from './broadcast_util';
 import {op} from './operation';
 import {scalar, zerosLike} from './tensor_ops';
 import {neg} from './unary_ops';
 
+
 /**
  * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
  *
diff --git a/tfjs-core/src/ops/fused_ops.ts b/tfjs-core/src/ops/fused_ops.ts
index 0977cbae2d4..9372292130f 100644
--- a/tfjs-core/src/ops/fused_ops.ts
+++ b/tfjs-core/src/ops/fused_ops.ts
@@ -25,12 +25,11 @@ import {convertToTensor} from '../tensor_util_env';
 import {TensorLike} from '../types';
 import * as util from '../util';
 
-import {add} from './binary_ops';
+import {add} from './add';
 import * as broadcast_util from './broadcast_util';
 import {conv2d as unfusedConv2d, depthwiseConv2d as unfusedDepthwiseConv2d} from './conv';
 import {Activation, shouldFuse} from './fused_util';
 import {matMul as unfusedMatMul} from './matmul';
-
 import {elu, prelu, relu, relu6} from './relu_ops';
 
 // Returns gradient for fused activation.

From ffcf45c3ea90cd03b17e2117020efd25f1c26419 Mon Sep 17 00:00:00 2001
From: Na Li <linazhao@google.com>
Date: Tue, 31 Mar 2020 13:21:28 -0700
Subject: [PATCH 3/7] .

---
 tfjs-core/src/gradients/Add_grad.ts  |   3 +-
 tfjs-core/src/ops/add.ts             |   6 +-
 tfjs-core/src/ops/add_test.ts        | 325 +++++++++++++++++++++++++++
 tfjs-core/src/ops/arithmetic_test.ts | 306 -------------------------
 tfjs-core/src/ops/binary_ops.ts      |   1 -
 5 files changed, 332 insertions(+), 309 deletions(-)
 create mode 100644 tfjs-core/src/ops/add_test.ts

diff --git a/tfjs-core/src/gradients/Add_grad.ts b/tfjs-core/src/gradients/Add_grad.ts
index 5d2bd450578..f1589528dc6 100644
--- a/tfjs-core/src/gradients/Add_grad.ts
+++ b/tfjs-core/src/gradients/Add_grad.ts
@@ -21,6 +21,7 @@ import {Tensor} from '../tensor';
 
 export const addGradConfig: GradConfig = {
   kernelName: Add,
+  inputsToSave: ['a', 'b'],
   gradFunc: (dy: Tensor, saved: Tensor[]) => {
     const [a, b] = saved;
     const outShape =
@@ -45,4 +46,4 @@ export const addGradConfig: GradConfig = {
 
     return {a: derA, b: derB};
   }
-}
+};
diff --git a/tfjs-core/src/ops/add.ts b/tfjs-core/src/ops/add.ts
index d7fb8c41d63..b96b7ca1abb 100644
--- a/tfjs-core/src/ops/add.ts
+++ b/tfjs-core/src/ops/add.ts
@@ -53,7 +53,11 @@ function add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
   let $b = convertToTensor(b, 'b', 'add');
   [$a, $b] = makeTypesMatch($a, $b);
 
-  const forward: ForwardFunc<Tensor> = backend => backend.add($a, $b);
+  const forward: ForwardFunc<Tensor> = (backend, save) => {
+    const res = backend.add($a, $b);
+    save([$a, $b]);
+    return res;
+  };
 
   const inputs: AddInputs = {a: $a, b: $b};
 
diff --git a/tfjs-core/src/ops/add_test.ts b/tfjs-core/src/ops/add_test.ts
new file mode 100644
index 00000000000..66101e5784c
--- /dev/null
+++ b/tfjs-core/src/ops/add_test.ts
@@ -0,0 +1,325 @@
+/**
+ * @license
+ * Copyright 2020 Google Inc. 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, expectArraysEqual} from '../test_util';
+
+describeWithFlags('add', ALL_ENVS, () => {
+  it('c + A', async () => {
+    const c = tf.scalar(5);
+    const a = tf.tensor1d([1, 2, 3]);
+
+    const result = tf.add(c, a);
+
+    expectArraysClose(await result.data(), [6, 7, 8]);
+  });
+
+  it('c + A propagates NaNs', async () => {
+    const c = tf.scalar(NaN);
+    const a = tf.tensor1d([1, 2, 3]);
+
+    const res = tf.add(c, a);
+
+    expectArraysEqual(await res.data(), [NaN, NaN, NaN]);
+  });
+
+  it('A + B broadcasting same rank Tensors different shape', async () => {
+    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+    const b = tf.tensor2d([2, 3], [2, 1]);
+
+    const result = tf.add(a, b);
+
+    expect(result.shape).toEqual([2, 2]);
+    const expected = [3, 4, 0, -1];
+
+    expectArraysClose(await result.data(), expected);
+  });
+
+  it('A + B broadcast 2D + 1D', async () => {
+    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+    const b = tf.tensor1d([1, 2]);
+
+    const result = tf.add(a, b);
+
+    expect(result.shape).toEqual([2, 2]);
+    const expected = [2, 4, -2, -2];
+
+    expectArraysClose(await result.data(), expected);
+  });
+
+  it('A + B', async () => {
+    const a = tf.tensor1d([2, 5, 1]);
+    const b = tf.tensor1d([4, 2, -1]);
+
+    const result = tf.add(a, b);
+
+    const expected = [6, 7, 0];
+    expectArraysClose(await result.data(), expected);
+  });
+
+  it('TensorLike', async () => {
+    const a = [2, 5, 1];
+    const b = [4, 2, -1];
+
+    const result = tf.add(a, b);
+
+    const expected = [6, 7, 0];
+    expectArraysClose(await result.data(), expected);
+  });
+
+  it('TensorLike chained', async () => {
+    const a = tf.tensor1d([2, 5, 1]);
+    const b = [4, 2, -1];
+
+    const result = a.add(b);
+
+    const expected = [6, 7, 0];
+    expectArraysClose(await result.data(), expected);
+  });
+
+  it('A + B propagates NaNs', async () => {
+    const a = tf.tensor1d([2, 5, NaN]);
+    const b = tf.tensor1d([4, 2, -1]);
+
+    const res = tf.add(a, b);
+    expectArraysClose(await res.data(), [6, 7, NaN]);
+  });
+
+  it('A + B throws when passed tensors with different shape', () => {
+    const a = tf.tensor1d([2, 5, 1, 5]);
+    const b = tf.tensor1d([4, 2, -1]);
+
+    expect(() => tf.add(a, b)).toThrowError();
+    expect(() => tf.add(b, a)).toThrowError();
+  });
+
+  it('2D+scalar broadcast', async () => {
+    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+    const b = tf.scalar(2);
+    const res = tf.add(a, b);
+    expect(res.shape).toEqual([2, 3]);
+    expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);
+  });
+
+  it('scalar+1D broadcast', async () => {
+    const a = tf.scalar(2);
+    const b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
+    const res = tf.add(a, b);
+    expect(res.shape).toEqual([6]);
+    expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);
+  });
+
+  it('2D+2D broadcast each with 1 dim', async () => {
+    const a = tf.tensor2d([1, 2, 5], [1, 3]);
+    const b = tf.tensor2d([7, 3], [2, 1]);
+    const res = tf.add(a, b);
+    expect(res.shape).toEqual([2, 3]);
+    expectArraysClose(await res.data(), [8, 9, 12, 4, 5, 8]);
+  });
+
+  it('2D+2D broadcast inner dim of b', async () => {
+    const a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
+    const b = tf.tensor2d([7, 3], [2, 1]);
+    const res = tf.add(a, b);
+    expect(res.shape).toEqual([2, 3]);
+    expectArraysClose(await res.data(), [8, 9, 12, 7, 8, 9]);
+  });
+
+  it('3D+scalar', async () => {
+    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
+    const b = tf.scalar(-1);
+    const res = tf.add(a, b);
+    expect(res.shape).toEqual([2, 3, 1]);
+    expectArraysClose(await res.data(), [0, 1, 2, 3, 4, 5]);
+  });
+
+  it('6D+scalar', async () => {
+    const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
+    const b = tf.scalar(-1);
+    const res = tf.add(a, b);
+    expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
+    const expectedResult = [
+      -1, 0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
+      15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+      31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
+      47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62
+    ];
+    expectArraysClose(await res.data(), expectedResult);
+  });
+
+  it('6D+2D', async () => {
+    const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
+    const b = tf.tensor2d([11, 13, 17, 19], [2, 2]);
+    const res = tf.add(a, b);
+    expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
+    const expectedResult = [
+      11, 14, 19, 22, 15, 18, 23, 26, 19, 22, 27, 30, 23, 26, 31, 34,
+      27, 30, 35, 38, 31, 34, 39, 42, 35, 38, 43, 46, 39, 42, 47, 50,
+      43, 46, 51, 54, 47, 50, 55, 58, 51, 54, 59, 62, 55, 58, 63, 66,
+      59, 62, 67, 70, 63, 66, 71, 74, 67, 70, 75, 78, 71, 74, 79, 82
+    ];
+    expectArraysClose(await res.data(), expectedResult);
+  });
+
+  it('add tensors with 0 in shape', async () => {
+    const a = tf.tensor1d([1]);
+    const b = tf.tensor3d([], [0, 0, 5]);
+    const res = tf.add(a, b);
+    expect(res.shape).toEqual([0, 0, 5]);
+    expectArraysEqual(await res.data(), []);
+  });
+
+  it('gradient: scalar + 1D broadcast', async () => {
+    const a = tf.scalar(2);
+    const b = tf.tensor1d([3, 4, 5]);
+    const dy = tf.tensor1d([7, 8, 9]);
+
+    const grads = tf.grads((a, b) => tf.add(a, b));
+    const [da, db] = grads([a, b], dy);
+
+    expect(da.shape).toEqual(a.shape);
+    expect(da.dtype).toEqual('float32');
+    expectArraysClose(await da.data(), [7 + 8 + 9]);
+
+    expect(db.shape).toEqual(b.shape);
+    expect(db.dtype).toEqual('float32');
+    expectArraysClose(await db.data(), [7, 8, 9]);
+  });
+
+  it('gradient with clones', async () => {
+    const a = tf.scalar(2);
+    const b = tf.tensor1d([3, 4, 5]);
+    const dy = tf.tensor1d([7, 8, 9]);
+
+    const grads = tf.grads((a, b) => tf.add(a.clone(), b.clone()).clone());
+    const [da, db] = grads([a, b], dy);
+
+    expect(da.shape).toEqual(a.shape);
+    expect(da.dtype).toEqual('float32');
+    expectArraysClose(await da.data(), [7 + 8 + 9]);
+
+    expect(db.shape).toEqual(b.shape);
+    expect(db.dtype).toEqual('float32');
+    expectArraysClose(await db.data(), [7, 8, 9]);
+  });
+
+  it('gradient: 2D + 2D broadcast', async () => {
+    const a = tf.tensor2d([2, 3], [2, 1]);
+    const b = tf.tensor2d([4, 5, 6, 7], [2, 2]);
+    const dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
+
+    const grads = tf.grads((a, b) => tf.add(a, b));
+    const [da, db] = grads([a, b], dy);
+
+    expect(da.shape).toEqual(a.shape);
+    expect(da.dtype).toEqual('float32');
+    expectArraysClose(await da.data(), [5 + 4, 3 + 2]);
+
+    expect(db.shape).toEqual(b.shape);
+    expect(db.dtype).toEqual('float32');
+    expectArraysClose(await db.data(), [5, 4, 3, 2]);
+  });
+
+  it('complex number addition', async () => {
+    const real1 = tf.tensor1d([1]);
+    const imag1 = tf.tensor1d([2]);
+    const complex1 = tf.complex(real1, imag1);
+
+    const real2 = tf.tensor1d([3]);
+    const imag2 = tf.tensor1d([4]);
+    const complex2 = tf.complex(real2, imag2);
+
+    const result = complex1.add(complex2);
+
+    expect(result.dtype).toBe('complex64');
+    expect(result.shape).toEqual([1]);
+    expectArraysClose(await result.data(), [4, 6]);
+  });
+
+  it('complex number reshape and then addition', async () => {
+    const real1 = tf.tensor1d([1]);
+    const imag1 = tf.tensor1d([2]);
+    const complex1 = tf.complex(real1, imag1);
+
+    const real2 = tf.tensor1d([3]);
+    const imag2 = tf.tensor1d([4]);
+    const complex2 = tf.complex(real2, imag2);
+
+    const complex1Reshaped = complex1.reshape([1, 1, 1]);
+    const complex2Reshaped = complex2.reshape([1, 1, 1]);
+
+    const result = complex1Reshaped.add(complex2Reshaped);
+
+    expect(result.dtype).toBe('complex64');
+    expect(result.shape).toEqual([1, 1, 1]);
+    expectArraysClose(await result.data(), [4, 6]);
+  });
+
+  it('complex number broadcasting addition', async () => {
+    const real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+    const imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
+    const complex1 = tf.complex(real1, imag1);
+
+    const real2 = tf.tensor1d([4]);
+    const imag2 = tf.tensor1d([5]);
+    const complex2 = tf.complex(real2, imag2);
+
+    const result = tf.add(complex1, complex2);
+
+    expect(result.dtype).toEqual('complex64');
+    expect(result.shape).toEqual([2, 2]);
+    expectArraysClose(
+        await result.data(),
+        [1 + 4, 10 + 5, 2 + 4, 20 + 5, -3 + 4, -30 + 5, -4 + 4, -40 + 5]);
+  });
+
+  it('throws when passed a as a non-tensor', () => {
+    expect(() => tf.add({} as tf.Tensor, tf.scalar(1)))
+        .toThrowError(/Argument 'a' passed to 'add' must be a Tensor/);
+  });
+  it('throws when passed b as a non-tensor', () => {
+    expect(() => tf.add(tf.scalar(1), {} as tf.Tensor))
+        .toThrowError(/Argument 'b' passed to 'add' must be a Tensor/);
+  });
+
+  it('upcasts when dtypes dont match', async () => {
+    let res = tf.add(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
+    expect(res.dtype).toBe('float32');
+    expectArraysClose(await res.data(), [2]);
+
+    res = tf.add(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
+    expect(res.dtype).toBe('int32');
+    expectArraysClose(await res.data(), [2]);
+
+    res = tf.add(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
+    expect(res.dtype).toBe('int32');
+    expectArraysClose(await res.data(), [1]);
+
+    res = tf.add(tf.complex(4, 7), tf.scalar(1, 'float32'));
+    expect(res.dtype).toBe('complex64');
+    expectArraysClose(await res.data(), [5, 7]);
+
+    res = tf.add(tf.complex(4, 7), tf.scalar(1, 'int32'));
+    expect(res.dtype).toBe('complex64');
+    expectArraysClose(await res.data(), [5, 7]);
+  });
+
+  it('accepts a tensor-like object', async () => {
+    const result = tf.add(5, [1, 2, 3]);
+    expectArraysClose(await result.data(), [6, 7, 8]);
+  });
+});
diff --git a/tfjs-core/src/ops/arithmetic_test.ts b/tfjs-core/src/ops/arithmetic_test.ts
index 3ae2dcc9b43..aeda0b01dbf 100644
--- a/tfjs-core/src/ops/arithmetic_test.ts
+++ b/tfjs-core/src/ops/arithmetic_test.ts
@@ -1014,312 +1014,6 @@ describeWithFlags('pow', ALL_ENVS, () => {
   });
 });
 
-describeWithFlags('add', ALL_ENVS, () => {
-  it('c + A', async () => {
-    const c = tf.scalar(5);
-    const a = tf.tensor1d([1, 2, 3]);
-
-    const result = tf.add(c, a);
-
-    expectArraysClose(await result.data(), [6, 7, 8]);
-  });
-
-  it('c + A propagates NaNs', async () => {
-    const c = tf.scalar(NaN);
-    const a = tf.tensor1d([1, 2, 3]);
-
-    const res = tf.add(c, a);
-
-    expectArraysEqual(await res.data(), [NaN, NaN, NaN]);
-  });
-
-  it('A + B broadcasting same rank Tensors different shape', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor2d([2, 3], [2, 1]);
-
-    const result = tf.add(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [3, 4, 0, -1];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('A + B broadcast 2D + 1D', async () => {
-    const a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const b = tf.tensor1d([1, 2]);
-
-    const result = tf.add(a, b);
-
-    expect(result.shape).toEqual([2, 2]);
-    const expected = [2, 4, -2, -2];
-
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('A + B', async () => {
-    const a = tf.tensor1d([2, 5, 1]);
-    const b = tf.tensor1d([4, 2, -1]);
-
-    const result = tf.add(a, b);
-
-    const expected = [6, 7, 0];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike', async () => {
-    const a = [2, 5, 1];
-    const b = [4, 2, -1];
-
-    const result = tf.add(a, b);
-
-    const expected = [6, 7, 0];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('TensorLike chained', async () => {
-    const a = tf.tensor1d([2, 5, 1]);
-    const b = [4, 2, -1];
-
-    const result = a.add(b);
-
-    const expected = [6, 7, 0];
-    expectArraysClose(await result.data(), expected);
-  });
-
-  it('A + B propagates NaNs', async () => {
-    const a = tf.tensor1d([2, 5, NaN]);
-    const b = tf.tensor1d([4, 2, -1]);
-
-    const res = tf.add(a, b);
-    expectArraysClose(await res.data(), [6, 7, NaN]);
-  });
-
-  it('A + B throws when passed tensors with different shape', () => {
-    const a = tf.tensor1d([2, 5, 1, 5]);
-    const b = tf.tensor1d([4, 2, -1]);
-
-    expect(() => tf.add(a, b)).toThrowError();
-    expect(() => tf.add(b, a)).toThrowError();
-  });
-
-  it('2D+scalar broadcast', async () => {
-    const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
-    const b = tf.scalar(2);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);
-  });
-
-  it('scalar+1D broadcast', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([6]);
-    expectArraysClose(await res.data(), [3, 4, 5, 6, 7, 8]);
-  });
-
-  it('2D+2D broadcast each with 1 dim', async () => {
-    const a = tf.tensor2d([1, 2, 5], [1, 3]);
-    const b = tf.tensor2d([7, 3], [2, 1]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [8, 9, 12, 4, 5, 8]);
-  });
-
-  it('2D+2D broadcast inner dim of b', async () => {
-    const a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
-    const b = tf.tensor2d([7, 3], [2, 1]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 3]);
-    expectArraysClose(await res.data(), [8, 9, 12, 7, 8, 9]);
-  });
-
-  it('3D+scalar', async () => {
-    const a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
-    const b = tf.scalar(-1);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 3, 1]);
-    expectArraysClose(await res.data(), [0, 1, 2, 3, 4, 5]);
-  });
-
-  it('6D+scalar', async () => {
-    const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
-    const b = tf.scalar(-1);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
-    const expectedResult = [
-      -1, 0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14,
-      15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
-      31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
-      47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62
-    ];
-    expectArraysClose(await res.data(), expectedResult);
-  });
-
-  it('6D+2D', async () => {
-    const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
-    const b = tf.tensor2d([11, 13, 17, 19], [2, 2]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
-    const expectedResult = [
-      11, 14, 19, 22, 15, 18, 23, 26, 19, 22, 27, 30, 23, 26, 31, 34,
-      27, 30, 35, 38, 31, 34, 39, 42, 35, 38, 43, 46, 39, 42, 47, 50,
-      43, 46, 51, 54, 47, 50, 55, 58, 51, 54, 59, 62, 55, 58, 63, 66,
-      59, 62, 67, 70, 63, 66, 71, 74, 67, 70, 75, 78, 71, 74, 79, 82
-    ];
-    expectArraysClose(await res.data(), expectedResult);
-  });
-
-  it('add tensors with 0 in shape', async () => {
-    const a = tf.tensor1d([1]);
-    const b = tf.tensor3d([], [0, 0, 5]);
-    const res = tf.add(a, b);
-    expect(res.shape).toEqual([0, 0, 5]);
-    expectArraysEqual(await res.data(), []);
-  });
-
-  it('gradient: scalar + 1D broadcast', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([7, 8, 9]);
-
-    const grads = tf.grads((a, b) => tf.add(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [7 + 8 + 9]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [7, 8, 9]);
-  });
-
-  it('gradient with clones', async () => {
-    const a = tf.scalar(2);
-    const b = tf.tensor1d([3, 4, 5]);
-    const dy = tf.tensor1d([7, 8, 9]);
-
-    const grads = tf.grads((a, b) => tf.add(a.clone(), b.clone()).clone());
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [7 + 8 + 9]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [7, 8, 9]);
-  });
-
-  it('gradient: 2D + 2D broadcast', async () => {
-    const a = tf.tensor2d([2, 3], [2, 1]);
-    const b = tf.tensor2d([4, 5, 6, 7], [2, 2]);
-    const dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
-
-    const grads = tf.grads((a, b) => tf.add(a, b));
-    const [da, db] = grads([a, b], dy);
-
-    expect(da.shape).toEqual(a.shape);
-    expect(da.dtype).toEqual('float32');
-    expectArraysClose(await da.data(), [5 + 4, 3 + 2]);
-
-    expect(db.shape).toEqual(b.shape);
-    expect(db.dtype).toEqual('float32');
-    expectArraysClose(await db.data(), [5, 4, 3, 2]);
-  });
-
-  it('complex number addition', async () => {
-    const real1 = tf.tensor1d([1]);
-    const imag1 = tf.tensor1d([2]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([3]);
-    const imag2 = tf.tensor1d([4]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const result = complex1.add(complex2);
-
-    expect(result.dtype).toBe('complex64');
-    expect(result.shape).toEqual([1]);
-    expectArraysClose(await result.data(), [4, 6]);
-  });
-
-  it('complex number reshape and then addition', async () => {
-    const real1 = tf.tensor1d([1]);
-    const imag1 = tf.tensor1d([2]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([3]);
-    const imag2 = tf.tensor1d([4]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const complex1Reshaped = complex1.reshape([1, 1, 1]);
-    const complex2Reshaped = complex2.reshape([1, 1, 1]);
-
-    const result = complex1Reshaped.add(complex2Reshaped);
-
-    expect(result.dtype).toBe('complex64');
-    expect(result.shape).toEqual([1, 1, 1]);
-    expectArraysClose(await result.data(), [4, 6]);
-  });
-
-  it('complex number broadcasting addition', async () => {
-    const real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
-    const imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
-    const complex1 = tf.complex(real1, imag1);
-
-    const real2 = tf.tensor1d([4]);
-    const imag2 = tf.tensor1d([5]);
-    const complex2 = tf.complex(real2, imag2);
-
-    const result = tf.add(complex1, complex2);
-
-    expect(result.dtype).toEqual('complex64');
-    expect(result.shape).toEqual([2, 2]);
-    expectArraysClose(
-        await result.data(),
-        [1 + 4, 10 + 5, 2 + 4, 20 + 5, -3 + 4, -30 + 5, -4 + 4, -40 + 5]);
-  });
-
-  it('throws when passed a as a non-tensor', () => {
-    expect(() => tf.add({} as tf.Tensor, tf.scalar(1)))
-        .toThrowError(/Argument 'a' passed to 'add' must be a Tensor/);
-  });
-  it('throws when passed b as a non-tensor', () => {
-    expect(() => tf.add(tf.scalar(1), {} as tf.Tensor))
-        .toThrowError(/Argument 'b' passed to 'add' must be a Tensor/);
-  });
-
-  it('upcasts when dtypes dont match', async () => {
-    let res = tf.add(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
-    expect(res.dtype).toBe('float32');
-    expectArraysClose(await res.data(), [2]);
-
-    res = tf.add(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [2]);
-
-    res = tf.add(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
-    expect(res.dtype).toBe('int32');
-    expectArraysClose(await res.data(), [1]);
-
-    res = tf.add(tf.complex(4, 7), tf.scalar(1, 'float32'));
-    expect(res.dtype).toBe('complex64');
-    expectArraysClose(await res.data(), [5, 7]);
-
-    res = tf.add(tf.complex(4, 7), tf.scalar(1, 'int32'));
-    expect(res.dtype).toBe('complex64');
-    expectArraysClose(await res.data(), [5, 7]);
-  });
-
-  it('accepts a tensor-like object', async () => {
-    const result = tf.add(5, [1, 2, 3]);
-    expectArraysClose(await result.data(), [6, 7, 8]);
-  });
-});
-
 describeWithFlags('addN', ALL_ENVS, () => {
   it('a single tensor', async () => {
     const res = tf.addN([tf.tensor1d([1, 2, 3])]);
diff --git a/tfjs-core/src/ops/binary_ops.ts b/tfjs-core/src/ops/binary_ops.ts
index a9455c24b8d..c44c82af29c 100644
--- a/tfjs-core/src/ops/binary_ops.ts
+++ b/tfjs-core/src/ops/binary_ops.ts
@@ -29,7 +29,6 @@ import {op} from './operation';
 import {scalar, zerosLike} from './tensor_ops';
 import {neg} from './unary_ops';
 
-
 /**
  * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
  *

From 09f0862658929fc65c0a3e0d27981f03e1b2d06a Mon Sep 17 00:00:00 2001
From: Na Li <linazhao@google.com>
Date: Tue, 31 Mar 2020 14:14:39 -0700
Subject: [PATCH 4/7] change build-deps and install order.

---
 tfjs-core/src/tests.ts | 1 +
 tfjs-data/package.json | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/tfjs-core/src/tests.ts b/tfjs-core/src/tests.ts
index ae9143836b5..a6515ae1622 100644
--- a/tfjs-core/src/tests.ts
+++ b/tfjs-core/src/tests.ts
@@ -37,6 +37,7 @@ import './io/router_registry_test';
 import './io/weights_loader_test';
 import './jasmine_util_test';
 import './kernel_registry_test';
+import './ops/add_test';
 import './ops/arithmetic_test';
 import './ops/array_ops_test';
 import './ops/axis_util_test';
diff --git a/tfjs-data/package.json b/tfjs-data/package.json
index 9ce9bd17dce..9a317e29ade 100644
--- a/tfjs-data/package.json
+++ b/tfjs-data/package.json
@@ -53,7 +53,7 @@
     "publish-npm": "npm publish",
     "test": "yarn && yarn build-deps && ts-node src/test_node.ts",
     "test-browsers": "karma start --browsers='Chrome,Firefox'",
-    "test-ci": "yarn && yarn build-deps-ci && yarn build-ci && yarn lint && ts-node src/test_node.ts",
+    "test-ci": "yarn build-deps-ci && yarn && yarn build-ci && yarn lint && ts-node src/test_node.ts",
     "test-snippets": "ts-node ./scripts/test_snippets.ts",
     "lint": "tslint -p . -t verbose"
   },

From fda7fbf79f55a85116f7b48316e44061d6520839 Mon Sep 17 00:00:00 2001
From: Na Li <linazhao@google.com>
Date: Tue, 31 Mar 2020 14:25:29 -0700
Subject: [PATCH 5/7] .

---
 tfjs-core/yarn.lock    | 27 +++++++++++++++++++++++----
 tfjs-data/package.json |  2 +-
 2 files changed, 24 insertions(+), 5 deletions(-)

diff --git a/tfjs-core/yarn.lock b/tfjs-core/yarn.lock
index 78de9633ccd..e708e93c2b3 100644
--- a/tfjs-core/yarn.lock
+++ b/tfjs-core/yarn.lock
@@ -18,10 +18,29 @@
     esutils "^2.0.2"
     js-tokens "^4.0.0"
 
-"@bazel/bazelisk@^1.3.0":
-  version "1.3.0"
-  resolved "https://registry.yarnpkg.com/@bazel/bazelisk/-/bazelisk-1.3.0.tgz#dc312dd30ad01e9af86e53b40795ab6e545fa55b"
-  integrity sha512-73H1nq3572tTf+dhDT86aWQN+LCyfxrh05jabqPXp6cpR8soxte3gS5oUqkN36fUe+J2HzNiV4CXZTz4Xytd3Q==
+"@bazel/bazel-darwin_x64@0.24.0":
+  version "0.24.0"
+  resolved "https://registry.yarnpkg.com/@bazel/bazel-darwin_x64/-/bazel-darwin_x64-0.24.0.tgz#828ef298d8d542961df388f17b0244f4f4302a74"
+  integrity sha512-xly44vkcD/fauUb7Lm5Lme4qhEZdkuuyBKSVQUHPbYAGDdbj/W8dupI3bZREkJAgG/WrRU+WXUemMj4U8ZcLcw==
+
+"@bazel/bazel-linux_x64@0.24.0":
+  version "0.24.0"
+  resolved "https://registry.yarnpkg.com/@bazel/bazel-linux_x64/-/bazel-linux_x64-0.24.0.tgz#9ef2e7266833ad2221fe4af4ceb6763d2897e3ff"
+  integrity sha512-p5ylPLWnJZDGbaIFBrtD/tp3Su5rMdzeeNJKU24XyiWQTHVZ3OD3I2Fb0ILCgfBjY8AlA7EtCtOI4hYnAuIOtg==
+
+"@bazel/bazel-win32_x64@0.24.0":
+  version "0.24.0"
+  resolved "https://registry.yarnpkg.com/@bazel/bazel-win32_x64/-/bazel-win32_x64-0.24.0.tgz#02d83113a6c6ed99795a3e41bff5631aa141638d"
+  integrity sha512-/bcSEx+GoV/q7H4WM0jazfxTcurSiIIePhRv+d05mxRDcaWwhCO8KzmmZRWH1abW6npvq5tLkbSQi7G7nUBhgg==
+
+"@bazel/bazel@^0.24.0":
+  version "0.24.0"
+  resolved "https://registry.yarnpkg.com/@bazel/bazel/-/bazel-0.24.0.tgz#f4e68e3680ac299858c24c26be3d08d1151e78fc"
+  integrity sha512-/5E55tqH9ogAGF9Dd7RSCJmk7/xdlsPTAhsX3yEsEMs7GLdHlgD3jbeePsKUiHKKr8LXAufjTs2pXQfjrkZRMg==
+  optionalDependencies:
+    "@bazel/bazel-darwin_x64" "0.24.0"
+    "@bazel/bazel-linux_x64" "0.24.0"
+    "@bazel/bazel-win32_x64" "0.24.0"
 
 "@bazel/typescript@^0.27.8":
   version "0.27.10"
diff --git a/tfjs-data/package.json b/tfjs-data/package.json
index 9a317e29ade..9ce9bd17dce 100644
--- a/tfjs-data/package.json
+++ b/tfjs-data/package.json
@@ -53,7 +53,7 @@
     "publish-npm": "npm publish",
     "test": "yarn && yarn build-deps && ts-node src/test_node.ts",
     "test-browsers": "karma start --browsers='Chrome,Firefox'",
-    "test-ci": "yarn build-deps-ci && yarn && yarn build-ci && yarn lint && ts-node src/test_node.ts",
+    "test-ci": "yarn && yarn build-deps-ci && yarn build-ci && yarn lint && ts-node src/test_node.ts",
     "test-snippets": "ts-node ./scripts/test_snippets.ts",
     "lint": "tslint -p . -t verbose"
   },

From 84541dab7772ea1a1b2e2472a7be7fcda9ad6c27 Mon Sep 17 00:00:00 2001
From: Na Li <linazhao@google.com>
Date: Tue, 31 Mar 2020 16:59:13 -0700
Subject: [PATCH 6/7] Fix lint.

---
 tfjs-core/src/gradients/Add_grad.ts | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tfjs-core/src/gradients/Add_grad.ts b/tfjs-core/src/gradients/Add_grad.ts
index f1589528dc6..a234a940cff 100644
--- a/tfjs-core/src/gradients/Add_grad.ts
+++ b/tfjs-core/src/gradients/Add_grad.ts
@@ -1,5 +1,3 @@
-import {Add} from '../kernel_names';
-import {GradConfig} from '../kernel_registry';
 /**
  * @license
  * Copyright 2020 Google Inc. All Rights Reserved.
@@ -16,6 +14,8 @@ import {GradConfig} from '../kernel_registry';
  * limitations under the License.
  * =============================================================================
  */
+import {Add} from '../kernel_names';
+import {GradConfig} from '../kernel_registry';
 import * as broadcast_util from '../ops/broadcast_util';
 import {Tensor} from '../tensor';
 

From 105f658ca9b62f3e4c091f8e17e41c31518bb15c Mon Sep 17 00:00:00 2001
From: Na Li <linazhao@google.com>
Date: Thu, 2 Apr 2020 10:23:05 -0700
Subject: [PATCH 7/7] .

---
 .../chained_ops/register_all_chained_ops_test.ts      | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts b/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts
index b82818be73c..d5901090a80 100644
--- a/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts
+++ b/tfjs-core/src/public/chained_ops/register_all_chained_ops_test.ts
@@ -24,13 +24,8 @@ import {ALL_ENVS, describeWithFlags} from '../../jasmine_util';
 // flexibility to change in future.
 
 const CHAINED_OPS = [
-<<<<<<< HEAD
-  'square', 'broadcastTo', 'tile', 'oneHot', 'div', 'divNoNan', 'transpose',
-  'pad', 'batchNorm'
-=======
-  'add', 'broadcastTo', 'div', 'divNoNan', 'oneHot', 'pad', 'square', 'tile',
-  'transpose'
->>>>>>> [core]Modularize add.
+  'add', 'batchNorm', 'broadcastTo', 'div', 'divNoNan', 'oneHot', 'pad',
+  'square', 'tile', 'transpose'
 ];
 
 describeWithFlags('chained ops', ALL_ENVS, () => {
@@ -39,7 +34,7 @@ describeWithFlags('chained ops', ALL_ENVS, () => {
     for (const opName of CHAINED_OPS) {
       //@ts-ignore
       expect(typeof tensor[opName])
-        .toBe('function', `${opName} chained op not found`);
+          .toBe('function', `${opName} chained op not found`);
     }
   });
 });