[core]Modularize add.

tfjs-core/src/gradients/Add_grad.ts

@@ -0,0 +1,49 @@
+/**
+ * @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 {Add} from '../kernel_names';
+import {GradConfig} from '../kernel_registry';
+import * as broadcast_util from '../ops/broadcast_util';
+import {Tensor} from '../tensor';
+
+export const addGradConfig: GradConfig = {
+  kernelName: Add,
+  inputsToSave: ['a', 'b'],
+  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};
+  }
+};

tfjs-core/src/kernel_names.ts

@@ -21,18 +21,21 @@
 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'>;
 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;
 

tfjs-core/src/ops/add.ts

@@ -0,0 +1,69 @@
+/**
+ * @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, $b);
+
+  const forward: ForwardFunc<Tensor> = (backend, save) => {
+    const res = backend.add($a, $b);
+    save([$a, $b]);
+    return res;
+  };
+
+  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_});