1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #include <memory> 17 #include <vector> 18 19 #include "tensorflow/compiler/xla/client/computation_builder.h" 20 #include "tensorflow/compiler/xla/client/global_data.h" 21 #include "tensorflow/compiler/xla/client/local_client.h" 22 #include "tensorflow/compiler/xla/shape_util.h" 23 #include "tensorflow/compiler/xla/status_macros.h" 24 #include "tensorflow/compiler/xla/statusor.h" 25 #include "tensorflow/compiler/xla/test_helpers.h" 26 #include "tensorflow/compiler/xla/tests/client_library_test_base.h" 27 #include "tensorflow/compiler/xla/tests/literal_test_util.h" 28 #include "tensorflow/compiler/xla/tests/test_macros.h" 29 #include "tensorflow/compiler/xla/tests/test_utils.h" 30 #include "tensorflow/compiler/xla/xla_data.pb.h" 31 #include "tensorflow/core/platform/test.h" 32 #include "tensorflow/core/platform/types.h" 33 34 namespace xla { 35 namespace { 36 37 class ClientTest : public ClientLibraryTestBase {}; 38 39 XLA_TEST_F(ClientTest, ExecuteWithLayout) { 40 ComputationBuilder b(client_, TestName()); 41 42 std::vector<std::vector<int64>> layouts = {{0, 1}, {1, 0}}; 43 for (const std::vector<int64>& execute_layout : layouts) { 44 for (const std::vector<int64>& transfer_layout : layouts) { 45 b.Add(b.ConstantR2<int32>({{1, 2}, {3, 4}}), 46 b.ConstantR2<int32>({{10, 20}, {30, 40}})); 47 TF_ASSERT_OK_AND_ASSIGN(auto computation, b.Build()); 48 49 ExecutionOptions execution_options = execution_options_; 50 *execution_options.mutable_shape_with_output_layout() = 51 ShapeUtil::MakeShapeWithLayout(S32, /*dimensions=*/{2, 2}, 52 execute_layout); 53 TF_ASSERT_OK_AND_ASSIGN( 54 std::unique_ptr<GlobalData> data, 55 client_->Execute(computation, {}, &execution_options)); 56 57 std::unique_ptr<Literal> expected_literal = 58 Literal::CreateR2WithLayout<int32>( 59 {{11, 22}, {33, 44}}, LayoutUtil::MakeLayout(transfer_layout)); 60 61 TF_ASSERT_OK_AND_ASSIGN( 62 auto computed, client_->Transfer(*data, &expected_literal->shape())); 63 64 LiteralTestUtil::AssertEqualShapesAndLayouts(expected_literal->shape(), 65 computed->shape()); 66 LiteralTestUtil::ExpectEqual(*expected_literal, *computed); 67 } 68 } 69 } 70 71 XLA_TEST_F(ClientTest, ExecuteWithTupleLayout) { 72 ComputationBuilder b(client_, TestName()); 73 74 b.Tuple({b.ConstantR2<int32>({{1, 2}, {3, 4}}), 75 b.ConstantR2<int32>({{10, 20}, {30, 40}})}); 76 77 TF_ASSERT_OK_AND_ASSIGN(auto computation, b.Build()); 78 79 ExecutionOptions execution_options = execution_options_; 80 // Create a result shape with one element column major and the other row 81 // major. 82 *execution_options.mutable_shape_with_output_layout() = 83 ShapeUtil::MakeTupleShape( 84 {ShapeUtil::MakeShapeWithLayout(S32, /*dimensions=*/{2, 2}, 85 /*minor_to_major=*/{0, 1}), 86 ShapeUtil::MakeShapeWithLayout(S32, /*dimensions=*/{2, 2}, 87 /*minor_to_major=*/{1, 0})}); 88 89 TF_ASSERT_OK_AND_ASSIGN( 90 auto result, 91 client_->ExecuteAndTransfer(computation, {}, &execution_options)); 92 LiteralTestUtil::ExpectR2Equal<int32>({{1, 2}, {3, 4}}, 93 LiteralView::Create(*result, {0})); 94 LiteralTestUtil::ExpectR2Equal<int32>({{10, 20}, {30, 40}}, 95 LiteralView::Create(*result, {1})); 96 97 EXPECT_TRUE(ShapeUtil::IsTuple(result->shape())); 98 EXPECT_EQ(2, ShapeUtil::TupleElementCount(result->shape())); 99 100 EXPECT_TRUE(ShapeUtil::Equal( 101 ShapeUtil::GetTupleElementShape(result->shape(), 0), 102 ShapeUtil::MakeShapeWithLayout(S32, /*dimensions=*/{2, 2}, 103 /*minor_to_major=*/{0, 1}))); 104 EXPECT_TRUE(ShapeUtil::Equal( 105 ShapeUtil::GetTupleElementShape(result->shape(), 1), 106 ShapeUtil::MakeShapeWithLayout(S32, /*dimensions=*/{2, 2}, 107 /*minor_to_major=*/{1, 0}))); 108 } 109 110 XLA_TEST_F(ClientTest, 111 DISABLED_ON_CPU_PARALLEL(DISABLED_ON_GPU(ExecuteParallel))) { 112 Computation add_with_one_arg, mul_with_two_args, dot_with_one_arg; 113 Shape shape = ShapeUtil::MakeShape(S32, {2, 2}); 114 115 TF_ASSERT_OK_AND_ASSIGN( 116 std::unique_ptr<GlobalData> const_arg, 117 client_->TransferToServer(*Literal::CreateR2<int32>({{5, 6}, {7, 8}}))); 118 119 ComputationBuilder b(client_, TestName() + ".add"); 120 b.Add(b.Parameter(0, shape, "param_0"), 121 b.ConstantR2<int32>({{1, 2}, {3, 4}})); 122 TF_ASSERT_OK_AND_ASSIGN(add_with_one_arg, b.Build()); 123 124 // We can't really test parallel execution on CPU since all of the cores in a 125 // CPU are presented as a single device. So for now we test "parallel" 126 // execution on a single device. 127 std::vector<Client::ComputationInstance> computation_instances; 128 TF_ASSERT_OK_AND_ASSIGN(std::vector<xla::DeviceHandle> devices, 129 client_->GetDeviceHandles(1)); 130 ASSERT_EQ(devices.size(), 1); 131 132 ExecutionOptions options = execution_options_; 133 *options.add_device_handles() = devices[0]; 134 computation_instances.push_back(Client::ComputationInstance( 135 add_with_one_arg, {const_arg.get()}, options, nullptr)); 136 137 TF_ASSERT_OK_AND_ASSIGN(auto results, 138 client_->ExecuteParallel(computation_instances)); 139 auto expected_result = Literal::CreateR2<int32>({{6, 8}, {10, 12}}); 140 141 TF_ASSERT_OK_AND_ASSIGN( 142 auto result_literal, 143 client_->Transfer(*results[0], &expected_result->shape())); 144 145 LiteralTestUtil::ExpectEqual(*expected_result, *result_literal); 146 } 147 148 } // namespace 149 } // namespace xla 150
