text: "use strict";
  /**
   * @license
   * Copyright 2019 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.
   * =============================================================================
   */
  var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
      return new (P || (P = Promise))(function (resolve, reject) {
          function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
          function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
          function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
          step((generator = generator.apply(thisArg, _arguments || [])).next());
      });
  };
  var __generator = (this && this.__generator) || function (thisArg, body) {
      var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
      return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
      function verb(n) { return function (v) { return step([n, v]); }; }
      function step(op) {
          if (f) throw new TypeError("Generator is already executing.");
          while (_) try {
              if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
              if (y = 0, t) op = [op[0] & 2, t.value];
              switch (op[0]) {
                  case 0: case 1: t = op; break;
                  case 4: _.label++; return { value: op[1], done: false };
                  case 5: _.label++; y = op[1]; op = [0]; continue;
                  case 7: op = _.ops.pop(); _.trys.pop(); continue;
                  default:
                      if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
                      if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
                      if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
                      if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
                      if (t[2]) _.ops.pop();
                      _.trys.pop(); continue;
              }
              op = body.call(thisArg, _);
          } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
          if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
      }
  };
  var _this = this;
  Object.defineProperty(exports, "__esModule", { value: true });
  var tf = require("../index");
  var jasmine_util_1 = require("../jasmine_util");
  var test_util_1 = require("../test_util");
  jasmine_util_1.describeWithFlags('fused matmul', jasmine_util_1.ALL_ENVS, function () {
      it('A x B', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      c = tf.fused.matMul({ a: a, b: b });
                      expect(c.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, c.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [0, 8, -3, 20]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, transposeA, transposeB, c, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      transposeA = false;
                      transposeB = false;
                      c = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: null, activation: 'relu' });
                      expect(c.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, c.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [0, 8, 0, 20]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with elu', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, transposeA, transposeB, c, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      transposeA = false;
                      transposeB = false;
                      c = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: null, activation: 'elu' });
                      expect(c.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, c.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [0, 8, -0.9502, 20]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu6', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, transposeA, transposeB, c, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      transposeA = false;
                      transposeB = false;
                      c = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: null, activation: 'relu6' });
                      expect(c.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, c.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [0, 6, 0, 6]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with prelu', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, alpha, transposeA, transposeB, c, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      alpha = tf.tensor2d([0.5, 0.5], [1, 2]);
                      transposeA = false;
                      transposeB = false;
                      c = tf.fused.matMul({
                          a: a,
                          b: b,
                          transposeA: transposeA,
                          transposeB: transposeB,
                          bias: null,
                          activation: 'prelu',
                          preluActivationWeights: alpha
                      });
                      expect(c.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, c.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [0, 8, -1.5, 20]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu transpose', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, transposeA, transposeB, c, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [2, 3]);
                      transposeA = false;
                      transposeB = true;
                      c = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: null, activation: 'relu' });
                      expect(c.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, c.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [0, 9, 0, 24]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu and bias', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, transposeA, transposeB, d, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
                      transposeA = false;
                      transposeB = false;
                      d = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: c, activation: 'relu' });
                      expect(d.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, d.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [1, 9, 0, 21]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu and broadcasted bias', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, act, transposeA, transposeB, d, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      c = tf.tensor1d([1, 1]);
                      act = 'relu';
                      transposeA = false;
                      transposeB = false;
                      d = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: c, activation: act });
                      expect(d.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, d.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [1, 9, 0, 21]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with elu and broadcasted bias', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, act, transposeA, transposeB, d, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      c = tf.tensor1d([1, 1]);
                      act = 'elu';
                      transposeA = false;
                      transposeB = false;
                      d = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: c, activation: act });
                      expect(d.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, d.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [1, 9, -0.8647, 21]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu and broadcasted bias different rank', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, act, transposeA, transposeB, d, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [2, 2, 3]);
                      b = tf.tensor3d([0, 1, -3, 2, 2, 1, 0, 1, -3, 2, 2, 1], [2, 3, 2]);
                      c = tf.tensor2d([1, 2], [1, 2]);
                      act = 'relu';
                      transposeA = false;
                      transposeB = false;
                      d = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: c, activation: act });
                      expect(d.shape).toEqual([2, 2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, d.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [2, 6, 0, 18, 0, 30, 0, 42]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with bias only', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, transposeA, transposeB, d, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
                      b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
                      c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
                      transposeA = false;
                      transposeB = false;
                      d = tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: c, activation: 'linear' });
                      expect(d.shape).toEqual([2, 2]);
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, d.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [1, 9, -2, 21]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu gradient', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, dy, transposeA, transposeB, grads, fusedGrads, _a, da, db, _b, fusedDa, fusedDb, _c, _d, _e, _f;
          return __generator(this, function (_g) {
              switch (_g.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
                      b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
                      dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
                      transposeA = false;
                      transposeB = false;
                      grads = tf.grads(function (a, b) {
                          var prod = tf.matMul(a, b, transposeA, transposeB);
                          return tf.relu(prod);
                      });
                      fusedGrads = tf.grads(function (a, b) {
                          return tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: null, activation: 'relu' });
                      });
                      _a = grads([a, b], dy), da = _a[0], db = _a[1];
                      _b = fusedGrads([a, b], dy), fusedDa = _b[0], fusedDb = _b[1];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, da.array()];
                  case 1:
                      _d = [_g.sent()];
                      return [4 /*yield*/, fusedDa.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_g.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, db.data()];
                  case 3:
                      _f = [_g.sent()];
                      return [4 /*yield*/, fusedDb.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_g.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
      it('gradient with clones A x B with relu', function () {
          var a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
          var b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
          var dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
          var transposeA = false;
          var transposeB = false;
          var fusedGrads = tf.grads(function (a, b) {
              return tf.fused
                  .matMul({
                  a: a.clone(),
                  b: b.clone(),
                  transposeA: transposeA,
                  transposeB: transposeB,
                  bias: null,
                  activation: 'relu'
              })
                  .clone();
          });
          var _a = fusedGrads([a, b], dy), fusedDa = _a[0], fusedDb = _a[1];
          expect(fusedDa.shape).toEqual(a.shape);
          expect(fusedDb.shape).toEqual(b.shape);
      });
      it('A x B with relu bias gradient', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, transposeA, transposeB, dy, grads, fusedGrads, _a, da, db, dc, _b, fusedDa, fusedDb, fusedDc, _c, _d, _e, _f, _g, _h;
          return __generator(this, function (_j) {
              switch (_j.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
                      b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
                      c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
                      transposeA = false;
                      transposeB = false;
                      dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
                      grads = tf.grads(function (a, b, c) {
                          var prod = tf.matMul(a, b, transposeA, transposeB);
                          var sum = tf.add(prod, c);
                          return tf.relu(sum);
                      });
                      fusedGrads = tf.grads(function (a, b, c) {
                          return tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: c, activation: 'relu' });
                      });
                      _a = grads([a, b, c], dy), da = _a[0], db = _a[1], dc = _a[2];
                      _b = fusedGrads([a, b, c], dy), fusedDa = _b[0], fusedDb = _b[1], fusedDc = _b[2];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, da.array()];
                  case 1:
                      _d = [_j.sent()];
                      return [4 /*yield*/, fusedDa.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_j.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, db.array()];
                  case 3:
                      _f = [_j.sent()];
                      return [4 /*yield*/, fusedDb.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_j.sent()]));
                      _g = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dc.array()];
                  case 5:
                      _h = [_j.sent()];
                      return [4 /*yield*/, fusedDc.array()];
                  case 6:
                      _g.apply(void 0, _h.concat([_j.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu bias gradient transpose', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, transposeA, transposeB, dy, grads, fusedGrads, _a, da, db, dc, _b, fusedDa, fusedDb, fusedDc, _c, _d, _e, _f, _g, _h;
          return __generator(this, function (_j) {
              switch (_j.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 10, 20, -30], [3, 2]);
                      b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
                      c = tf.tensor2d([1, 1, 1, 1], [2, 2]);
                      transposeA = true;
                      transposeB = false;
                      dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
                      grads = tf.grads(function (a, b, c) {
                          var prod = tf.matMul(a, b, transposeA, transposeB);
                          var sum = tf.add(prod, c);
                          return tf.relu(sum);
                      });
                      fusedGrads = tf.grads(function (a, b, c) {
                          return tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: c, activation: 'relu' });
                      });
                      _a = grads([a, b, c], dy), da = _a[0], db = _a[1], dc = _a[2];
                      _b = fusedGrads([a, b, c], dy), fusedDa = _b[0], fusedDb = _b[1], fusedDc = _b[2];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, da.array()];
                  case 1:
                      _d = [_j.sent()];
                      return [4 /*yield*/, fusedDa.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_j.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, db.array()];
                  case 3:
                      _f = [_j.sent()];
                      return [4 /*yield*/, fusedDb.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_j.sent()]));
                      _g = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dc.array()];
                  case 5:
                      _h = [_j.sent()];
                      return [4 /*yield*/, fusedDc.array()];
                  case 6:
                      _g.apply(void 0, _h.concat([_j.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
      it('A x B with relu and broadcasted bias gradient', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, c, transposeA, transposeB, dy, grads, fusedGrads, _a, da, db, dc, _b, fusedDa, fusedDb, fusedDc, _c, _d, _e, _f, _g, _h;
          return __generator(this, function (_j) {
              switch (_j.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
                      b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
                      c = tf.tensor2d([[1]]);
                      transposeA = false;
                      transposeB = false;
                      dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
                      grads = tf.grads(function (a, b, c) {
                          var prod = tf.matMul(a, b, transposeA, transposeB);
                          var sum = tf.add(prod, c);
                          return tf.relu(sum);
                      });
                      fusedGrads = tf.grads(function (a, b, c) {
                          return tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: c, activation: 'relu' });
                      });
                      _a = grads([a, b, c], dy), da = _a[0], db = _a[1], dc = _a[2];
                      _b = fusedGrads([a, b, c], dy), fusedDa = _b[0], fusedDb = _b[1], fusedDc = _b[2];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, da.array()];
                  case 1:
                      _d = [_j.sent()];
                      return [4 /*yield*/, fusedDa.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_j.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, db.array()];
                  case 3:
                      _f = [_j.sent()];
                      return [4 /*yield*/, fusedDb.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_j.sent()]));
                      _g = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dc.array()];
                  case 5:
                      _h = [_j.sent()];
                      return [4 /*yield*/, fusedDc.array()];
                  case 6:
                      _g.apply(void 0, _h.concat([_j.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
  });
  jasmine_util_1.describeWithFlags('fused depthwiseConv2d', jasmine_util_1.ALL_ENVS, function () {
      it('basic', function () { return __awaiter(_this, void 0, void 0, function () {
          var fSize, pad, strides, chMul, inDepth, x, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      fSize = 2;
                      pad = 'valid';
                      strides = 1;
                      chMul = 1;
                      inDepth = 1;
                      x = tf.tensor4d([
                          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
                          0.0741907, 0.409265, 0.351377
                      ], [1, 3, 3, inDepth]);
                      w = tf.tensor4d([-0.303873, -0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
                      result = tf.fused.depthwiseConv2d({ x: x, filter: w, strides: strides, pad: pad });
                      expect(result.shape).toEqual([1, 2, 2, 1]);
                      expected = [0.47737, 0.40018, 0.00859, -0.09615];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('basic with relu', function () { return __awaiter(_this, void 0, void 0, function () {
          var fSize, pad, strides, chMul, inDepth, x, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      fSize = 2;
                      pad = 'valid';
                      strides = 1;
                      chMul = 1;
                      inDepth = 1;
                      x = tf.tensor4d([
                          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
                          0.0741907, 0.409265, 0.351377
                      ], [1, 3, 3, inDepth]);
                      w = tf.tensor4d([-0.303873, -0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
                      result = tf.fused.depthwiseConv2d({ x: x, filter: w, strides: strides, pad: pad, activation: 'relu' });
                      expect(result.shape).toEqual([1, 2, 2, 1]);
                      expected = [0.47737, 0.40018, 0.00859, 0];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('basic with bias and relu', function () { return __awaiter(_this, void 0, void 0, function () {
          var fSize, pad, strides, chMul, inDepth, x, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      fSize = 2;
                      pad = 'valid';
                      strides = 1;
                      chMul = 1;
                      inDepth = 1;
                      x = tf.tensor4d([
                          0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
                          0.0741907, 0.409265, 0.351377
                      ], [1, 3, 3, inDepth]);
                      w = tf.tensor4d([-0.303873, -0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
                      result = tf.fused.depthwiseConv2d({ x: x, filter: w, strides: strides, pad: pad, bias: tf.scalar(1), activation: 'relu' });
                      expect(result.shape).toEqual([1, 2, 2, 1]);
                      expected = [1.47737, 1.40018, 1.00859, 0.90385];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('prelu', function () { return __awaiter(_this, void 0, void 0, function () {
          var fSize, pad, strides, chMul, inDepth, x, alpha, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      fSize = 3;
                      pad = 'valid';
                      strides = 1;
                      chMul = 1;
                      inDepth = 1;
                      x = tf.tensor4d([
                          0.149194, 0.089009, 0.654891, 0.083324, 0.537043, 0.644331, 0.563037,
                          0.211859, 0.633501, 0.186427, 0.777034, 0.50001, 0.607341, 0.95303,
                          0.696479, 0.050387, 0.62045, 0.728049, 0.028043, 0.437009, 0.712881,
                          0.741935, 0.974474, 0.621102, 0.171411
                      ], [1, 5, 5, inDepth]);
                      alpha = tf.tensor4d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], [1, 3, 3, 1]);
                      w = tf.tensor4d([
                          -0.125386, -0.975199, -0.640437, -0.281895, -0.990968, -0.347208,
                          -0.889702, -0.180695, -0.691992
                      ], [fSize, fSize, inDepth, chMul]);
                      result = tf.fused.depthwiseConv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          activation: 'prelu',
                          preluActivationWeights: alpha
                      });
                      expect(result.shape).toEqual([1, 3, 3, 1]);
                      expected = [
                          -0.25400, -0.50118, -0.73622, -0.94068, -1.2298, -1.84585, -2.3089,
                          -2.7499, -2.64077
                      ];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, outputDepth, inputShape, filterSize, strides, pad, filterShape, filter, x, dy, grads, _a, dx, dfilter, _b, _c;
          return __generator(this, function (_d) {
              switch (_d.label) {
                  case 0:
                      inputDepth = 1;
                      outputDepth = 1;
                      inputShape = [2, 3, 3, inputDepth];
                      filterSize = 2;
                      strides = 1;
                      pad = 0;
                      filterShape = [filterSize, filterSize, inputDepth, outputDepth];
                      filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
                      dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
                      grads = tf.grads(function (x, filter) {
                          return tf.fused.depthwiseConv2d({ x: x, filter: filter, strides: strides, pad: pad });
                      });
                      _a = grads([x, filter], dy), dx = _a[0], dfilter = _a[1];
                      expect(dx.shape).toEqual(x.shape);
                      _b = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dx.data()];
                  case 1:
                      _b.apply(void 0, [_d.sent(),
                          [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]]);
                      expect(dfilter.shape).toEqual(filterShape);
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dfilter.data()];
                  case 2:
                      _c.apply(void 0, [_d.sent(), [26, 38, 62, 74]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, outputDepth, inputShape, filterSize, strides, pad, filterShape, filter, bias, x, dy, fusedGrads, _a, dxFused, dfilterFused, dbiasFused, grads, _b, dx, dfilter, dbias, _c, _d, _e, _f, _g, _h;
          return __generator(this, function (_j) {
              switch (_j.label) {
                  case 0:
                      inputDepth = 1;
                      outputDepth = 1;
                      inputShape = [2, 3, 3, inputDepth];
                      filterSize = 2;
                      strides = 1;
                      pad = 0;
                      filterShape = [filterSize, filterSize, inputDepth, outputDepth];
                      filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
                      bias = tf.ones([2, 2, 2, 1]);
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
                      dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
                      fusedGrads = tf.grads(function (x, w, b) { return tf.fused.depthwiseConv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          bias: b
                      }); });
                      _a = fusedGrads([x, filter, bias], dy), dxFused = _a[0], dfilterFused = _a[1], dbiasFused = _a[2];
                      grads = tf.grads(function (x, filter, bias) {
                          var conv = tf.depthwiseConv2d(x, filter, strides, pad);
                          var sum = tf.add(conv, bias);
                          return sum;
                      });
                      _b = grads([x, filter, bias], dy), dx = _b[0], dfilter = _b[1], dbias = _b[2];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dxFused.array()];
                  case 1:
                      _d = [_j.sent()];
                      return [4 /*yield*/, dx.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_j.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dfilterFused.array()];
                  case 3:
                      _f = [_j.sent()];
                      return [4 /*yield*/, dfilter.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_j.sent()]));
                      _g = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dbiasFused.array()];
                  case 5:
                      _h = [_j.sent()];
                      return [4 /*yield*/, dbias.array()];
                  case 6:
                      _g.apply(void 0, _h.concat([_j.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
      it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and activation', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, outputDepth, inputShape, filterSize, strides, pad, filterShape, filter, bias, x, dy, fusedGrads, _a, dxFused, dfilterFused, dbiasFused, grads, _b, dx, dfilter, dbias, _c, _d, _e, _f, _g, _h;
          return __generator(this, function (_j) {
              switch (_j.label) {
                  case 0:
                      inputDepth = 1;
                      outputDepth = 1;
                      inputShape = [2, 3, 3, inputDepth];
                      filterSize = 2;
                      strides = 1;
                      pad = 0;
                      filterShape = [filterSize, filterSize, inputDepth, outputDepth];
                      filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
                      bias = tf.ones([2, 2, 2, 1]);
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
                      dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
                      fusedGrads = tf.grads(function (x, w, b) { return tf.fused.depthwiseConv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          bias: b,
                          activation: 'relu'
                      }); });
                      _a = fusedGrads([x, filter, bias], dy), dxFused = _a[0], dfilterFused = _a[1], dbiasFused = _a[2];
                      grads = tf.grads(function (x, filter, bias) {
                          var conv = tf.depthwiseConv2d(x, filter, strides, pad);
                          var sum = tf.add(conv, bias);
                          return tf.relu(sum);
                      });
                      _b = grads([x, filter, bias], dy), dx = _b[0], dfilter = _b[1], dbias = _b[2];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dxFused.array()];
                  case 1:
                      _d = [_j.sent()];
                      return [4 /*yield*/, dx.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_j.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dfilterFused.array()];
                  case 3:
                      _f = [_j.sent()];
                      return [4 /*yield*/, dfilter.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_j.sent()]));
                      _g = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dbiasFused.array()];
                  case 5:
                      _h = [_j.sent()];
                      return [4 /*yield*/, dbias.array()];
                  case 6:
                      _g.apply(void 0, _h.concat([_j.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
  });
  jasmine_util_1.describeWithFlags('fused conv2d', jasmine_util_1.ALL_ENVS, function () {
      it('basic', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inShape, outputDepth, fSize, pad, stride, x, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 2;
                      inShape = [2, 2, 2, inputDepth];
                      outputDepth = 2;
                      fSize = 1;
                      pad = 0;
                      stride = 1;
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
                      w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
                      result = tf.fused.conv2d({ x: x, filter: w, strides: stride, pad: pad });
                      expect(result.shape).toEqual([2, 2, 2, 2]);
                      expected = [-5, 2, -11, 5, -17, 8, -23, 11, -29, 14, -35, 17, -41, 20, -47, 23];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('basic with relu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inShape, outputDepth, fSize, pad, stride, x, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 2;
                      inShape = [2, 2, 2, inputDepth];
                      outputDepth = 2;
                      fSize = 1;
                      pad = 0;
                      stride = 1;
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
                      w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
                      result = tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: stride,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          activation: 'relu'
                      });
                      expect(result.shape).toEqual([2, 2, 2, 2]);
                      expected = [0, 2, 0, 5, 0, 8, 0, 11, 0, 14, 0, 17, 0, 20, 0, 23];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('basic with elu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inShape, outputDepth, fSize, pad, stride, x, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 2;
                      inShape = [2, 2, 2, inputDepth];
                      outputDepth = 2;
                      fSize = 1;
                      pad = 0;
                      stride = 1;
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
                      w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
                      result = tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: stride,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          activation: 'elu'
                      });
                      expect(result.shape).toEqual([2, 2, 2, 2]);
                      expected = [-0.99326, 2, -1, 5, -1, 8, -1, 11, -1, 14, -1, 17, -1, 20, -1, 23];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('basic with prelu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inShape, outputDepth, fSize, pad, stride, x, alpha, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 2;
                      inShape = [2, 2, 2, inputDepth];
                      outputDepth = 2;
                      fSize = 1;
                      pad = 0;
                      stride = 1;
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
                      alpha = tf.tensor3d([0.25, 0.75], [1, 1, 2]);
                      w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
                      result = tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: stride,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          activation: 'prelu',
                          preluActivationWeights: alpha
                      });
                      expect(result.shape).toEqual([2, 2, 2, 2]);
                      expected = [
                          -1.25, 2, -2.75, 5, -4.25, 8, -5.75, 11, -7.25, 14, -8.75, 17, -10.25, 20,
                          -11.75, 23
                      ];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('basic with bias and relu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inShape, outputDepth, fSize, pad, stride, x, w, result, expected, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 2;
                      inShape = [2, 2, 2, inputDepth];
                      outputDepth = 2;
                      fSize = 1;
                      pad = 0;
                      stride = 1;
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inShape);
                      w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, outputDepth]);
                      result = tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: stride,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          bias: tf.scalar(5),
                          activation: 'relu'
                      });
                      expect(result.shape).toEqual([2, 2, 2, 2]);
                      expected = [0, 7, 0, 10, 0, 13, 0, 16, 0, 19, 0, 22, 0, 25, 0, 28];
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), expected]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('im2row', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inputShape, outputDepth, fSize, pad, strides, x, w, result, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 1;
                      inputShape = [4, 4, inputDepth];
                      outputDepth = 3;
                      fSize = 1;
                      pad = 'same';
                      strides = [2, 2];
                      x = tf.tensor3d([
                          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
                      ], inputShape);
                      w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
                      result = tf.fused.conv2d({ x: x, filter: w, strides: strides, pad: pad });
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(),
                          [10, 5, 10, 50, 25, 50, -10, -5, -10, -50, -25, -50]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('im2row with relu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inputShape, outputDepth, fSize, pad, strides, x, w, result, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 1;
                      inputShape = [4, 4, inputDepth];
                      outputDepth = 3;
                      fSize = 1;
                      pad = 'same';
                      strides = [2, 2];
                      x = tf.tensor3d([
                          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
                      ], inputShape);
                      w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
                      result = tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          activation: 'relu'
                      });
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [10, 5, 10, 50, 25, 50, 0, 0, 0, 0, 0, 0]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('im2row with prelu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inputShape, outputDepth, fSize, pad, strides, x, w, alpha, result, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 1;
                      inputShape = [4, 4, inputDepth];
                      outputDepth = 3;
                      fSize = 1;
                      pad = 'same';
                      strides = [2, 2];
                      x = tf.tensor3d([
                          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
                      ], inputShape);
                      w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
                      alpha = tf.tensor3d([0.5], [1, 1, inputDepth]);
                      result = tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          activation: 'prelu',
                          preluActivationWeights: alpha
                      });
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(),
                          [10, 5, 10, 50, 25, 50, -5, -2.5, -5, -25, -12.5, -25]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('pointwise with prelu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inputShape, outputDepth, fSize, pad, strides, x, w, alpha, result, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 1;
                      inputShape = [4, 4, inputDepth];
                      outputDepth = 3;
                      fSize = 1;
                      pad = 'same';
                      strides = [1, 1];
                      x = tf.tensor3d([
                          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
                      ], inputShape);
                      w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
                      alpha = tf.tensor3d([0.5], [1, 1, inputDepth]);
                      result = tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          activation: 'prelu',
                          preluActivationWeights: alpha
                      });
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [
                              10, 5, 10, 30, 15, 30, 50, 25, 50, 70, 35, 70,
                              20, 10, 20, 40, 20, 40, 60, 30, 60, 80, 40, 80,
                              -5, -2.5, -5, -15, -7.5, -15, -25, -12.5, -25, -35, -17.5, -35,
                              -10, -5, -10, -20, -10, -20, -30, -15, -30, -40, -20, -40
                          ]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('im2row with bias and relu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, inputShape, outputDepth, fSize, pad, strides, x, w, result, _a;
          return __generator(this, function (_b) {
              switch (_b.label) {
                  case 0:
                      inputDepth = 1;
                      inputShape = [4, 4, inputDepth];
                      outputDepth = 3;
                      fSize = 1;
                      pad = 'same';
                      strides = [2, 2];
                      x = tf.tensor3d([
                          10, 30, 50, 70, 20, 40, 60, 80, -10, -30, -50, -70, -20, -40, -60, -80
                      ], inputShape);
                      w = tf.tensor4d([1, 0.5, 1], [fSize, fSize, inputDepth, outputDepth]);
                      result = tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          bias: tf.scalar(5),
                          activation: 'relu'
                      });
                      _a = test_util_1.expectArraysClose;
                      return [4 /*yield*/, result.data()];
                  case 1:
                      _a.apply(void 0, [_b.sent(), [15, 10, 15, 55, 30, 55, 0, 0, 0, 0, 0, 0]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, outputDepth, inputShape, filterSize, strides, pad, filterShape, filter, x, dy, grads, _a, dx, dfilter, _b, _c;
          return __generator(this, function (_d) {
              switch (_d.label) {
                  case 0:
                      inputDepth = 1;
                      outputDepth = 1;
                      inputShape = [2, 3, 3, inputDepth];
                      filterSize = 2;
                      strides = 1;
                      pad = 0;
                      filterShape = [filterSize, filterSize, inputDepth, outputDepth];
                      filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
                      dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
                      grads = tf.grads(function (x, filter) {
                          return tf.fused.conv2d({ x: x, filter: filter, strides: strides, pad: pad });
                      });
                      _a = grads([x, filter], dy), dx = _a[0], dfilter = _a[1];
                      expect(dx.shape).toEqual(x.shape);
                      _b = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dx.data()];
                  case 1:
                      _b.apply(void 0, [_d.sent(),
                          [-3, 2, 1, -8, 1.5, 0.5, -4, 1, 0, -3, 2, 1, -8, 1.5, 0.5, -4, 1, 0]]);
                      expect(dfilter.shape).toEqual(filterShape);
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dfilter.data()];
                  case 2:
                      _c.apply(void 0, [_d.sent(), [26, 38, 62, 74]]);
                      return [2 /*return*/];
              }
          });
      }); });
      it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, outputDepth, inputShape, filterSize, strides, pad, filterShape, filter, bias, x, dy, fusedGrads, _a, dxFused, dfilterFused, dbiasFused, grads, _b, dx, dfilter, dbias, _c, _d, _e, _f, _g, _h;
          return __generator(this, function (_j) {
              switch (_j.label) {
                  case 0:
                      inputDepth = 1;
                      outputDepth = 1;
                      inputShape = [2, 3, 3, inputDepth];
                      filterSize = 2;
                      strides = 1;
                      pad = 0;
                      filterShape = [filterSize, filterSize, inputDepth, outputDepth];
                      filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
                      bias = tf.ones([2, 2, 2, 1]);
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
                      dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
                      fusedGrads = tf.grads(function (x, w, b) { return tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          bias: b
                      }); });
                      _a = fusedGrads([x, filter, bias], dy), dxFused = _a[0], dfilterFused = _a[1], dbiasFused = _a[2];
                      grads = tf.grads(function (x, filter, bias) {
                          var conv = tf.conv2d(x, filter, strides, pad);
                          var sum = tf.add(conv, bias);
                          return sum;
                      });
                      _b = grads([x, filter, bias], dy), dx = _b[0], dfilter = _b[1], dbias = _b[2];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dxFused.array()];
                  case 1:
                      _d = [_j.sent()];
                      return [4 /*yield*/, dx.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_j.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dfilterFused.array()];
                  case 3:
                      _f = [_j.sent()];
                      return [4 /*yield*/, dfilter.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_j.sent()]));
                      _g = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dbiasFused.array()];
                  case 5:
                      _h = [_j.sent()];
                      return [4 /*yield*/, dbias.array()];
                  case 6:
                      _g.apply(void 0, _h.concat([_j.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
      it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and relu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, outputDepth, inputShape, filterSize, strides, pad, filterShape, filter, bias, x, dy, fusedGrads, _a, dxFused, dfilterFused, dbiasFused, grads, _b, dx, dfilter, dbias, _c, _d, _e, _f, _g, _h;
          return __generator(this, function (_j) {
              switch (_j.label) {
                  case 0:
                      inputDepth = 1;
                      outputDepth = 1;
                      inputShape = [2, 3, 3, inputDepth];
                      filterSize = 2;
                      strides = 1;
                      pad = 0;
                      filterShape = [filterSize, filterSize, inputDepth, outputDepth];
                      filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
                      bias = tf.ones([2, 2, 2, 1]);
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
                      dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
                      fusedGrads = tf.grads(function (x, w, b) { return tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          bias: b,
                          activation: 'relu'
                      }); });
                      _a = fusedGrads([x, filter, bias], dy), dxFused = _a[0], dfilterFused = _a[1], dbiasFused = _a[2];
                      grads = tf.grads(function (x, filter, bias) {
                          var conv = tf.conv2d(x, filter, strides, pad);
                          var sum = tf.add(conv, bias);
                          return tf.relu(sum);
                      });
                      _b = grads([x, filter, bias], dy), dx = _b[0], dfilter = _b[1], dbias = _b[2];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dxFused.array()];
                  case 1:
                      _d = [_j.sent()];
                      return [4 /*yield*/, dx.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_j.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dfilterFused.array()];
                  case 3:
                      _f = [_j.sent()];
                      return [4 /*yield*/, dfilter.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_j.sent()]));
                      _g = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dbiasFused.array()];
                  case 5:
                      _h = [_j.sent()];
                      return [4 /*yield*/, dbias.array()];
                  case 6:
                      _g.apply(void 0, _h.concat([_j.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
      it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0 with bias and elu', function () { return __awaiter(_this, void 0, void 0, function () {
          var inputDepth, outputDepth, inputShape, filterSize, strides, pad, filterShape, filter, bias, x, dy, fusedGrads, _a, dxFused, dfilterFused, dbiasFused, grads, _b, dx, dfilter, dbias, _c, _d, _e, _f, _g, _h;
          return __generator(this, function (_j) {
              switch (_j.label) {
                  case 0:
                      inputDepth = 1;
                      outputDepth = 1;
                      inputShape = [2, 3, 3, inputDepth];
                      filterSize = 2;
                      strides = 1;
                      pad = 0;
                      filterShape = [filterSize, filterSize, inputDepth, outputDepth];
                      filter = tf.tensor4d([-1, 1, -2, 0.5], filterShape);
                      bias = tf.ones([2, 2, 2, 1]);
                      x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
                      dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
                      fusedGrads = tf.grads(function (x, w, b) { return tf.fused.conv2d({
                          x: x,
                          filter: w,
                          strides: strides,
                          pad: pad,
                          dataFormat: 'NHWC',
                          dilations: [1, 1],
                          bias: b,
                          activation: 'elu'
                      }); });
                      _a = fusedGrads([x, filter, bias], dy), dxFused = _a[0], dfilterFused = _a[1], dbiasFused = _a[2];
                      grads = tf.grads(function (x, filter, bias) {
                          var conv = tf.conv2d(x, filter, strides, pad);
                          var sum = tf.add(conv, bias);
                          return tf.elu(sum);
                      });
                      _b = grads([x, filter, bias], dy), dx = _b[0], dfilter = _b[1], dbias = _b[2];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dxFused.array()];
                  case 1:
                      _d = [_j.sent()];
                      return [4 /*yield*/, dx.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_j.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dfilterFused.array()];
                  case 3:
                      _f = [_j.sent()];
                      return [4 /*yield*/, dfilter.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_j.sent()]));
                      _g = test_util_1.expectArraysClose;
                      return [4 /*yield*/, dbiasFused.array()];
                  case 5:
                      _h = [_j.sent()];
                      return [4 /*yield*/, dbias.array()];
                  case 6:
                      _g.apply(void 0, _h.concat([_j.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
      it('fused matmul with relu6', function () { return __awaiter(_this, void 0, void 0, function () {
          var a, b, dy, transposeA, transposeB, fusedGrads, _a, fusedDa, fusedDb, grads, _b, da, db, _c, _d, _e, _f;
          return __generator(this, function (_g) {
              switch (_g.label) {
                  case 0:
                      a = tf.tensor2d([1, 2, 3, 10, 20, -30], [2, 3]);
                      b = tf.tensor2d([2, 3, 4, -1, 2, 3], [3, 2]);
                      dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
                      transposeA = false;
                      transposeB = false;
                      fusedGrads = tf.grads(function (a, b) {
                          return tf.fused.matMul({ a: a, b: b, transposeA: transposeA, transposeB: transposeB, bias: null, activation: 'relu6' });
                      });
                      _a = fusedGrads([a, b], dy), fusedDa = _a[0], fusedDb = _a[1];
                      grads = tf.grads(function (a, b) {
                          var prod = tf.matMul(a, b, transposeA, transposeB);
                          return tf.relu6(prod);
                      });
                      _b = grads([a, b], dy), da = _b[0], db = _b[1];
                      _c = test_util_1.expectArraysClose;
                      return [4 /*yield*/, da.array()];
                  case 1:
                      _d = [_g.sent()];
                      return [4 /*yield*/, fusedDa.array()];
                  case 2:
                      _c.apply(void 0, _d.concat([_g.sent()]));
                      _e = test_util_1.expectArraysClose;
                      return [4 /*yield*/, db.data()];
                  case 3:
                      _f = [_g.sent()];
                      return [4 /*yield*/, fusedDb.array()];
                  case 4:
                      _e.apply(void 0, _f.concat([_g.sent()]));
                      return [2 /*return*/];
              }
          });
      }); });
  });
  //# sourceMappingURL=fused_test.js.map