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 #include "tensorflow/compiler/tf2xla/sharding_util.h"
16
17 #include <functional>
18
19 #include "tensorflow/core/lib/core/status_test_util.h"
20 #include "tensorflow/core/platform/test.h"
21
22 namespace tensorflow {
23
TEST(CoreUtilTest,ParseShardingFromDevice)24 TEST(CoreUtilTest, ParseShardingFromDevice) {
25 Graph graph(OpRegistry::Global());
26
27 auto core_from_sharding =
28 [](std::optional<xla::OpSharding> sharding) -> int64 {
29 if (sharding.has_value() &&
30 sharding.value().type() == xla::OpSharding::MAXIMAL) {
31 return sharding.value().tile_assignment_devices(0);
32 } else {
33 return -1;
34 }
35 };
36
37 auto parse_status = ParseShardingFromDevice("", 1);
38 TF_EXPECT_OK(parse_status.status());
39 EXPECT_EQ(-1, core_from_sharding(parse_status.ValueOrDie()));
40 parse_status = ParseShardingFromDevice("", 100);
41 TF_EXPECT_OK(parse_status.status());
42 EXPECT_EQ(-1, core_from_sharding(parse_status.ValueOrDie()));
43
44 parse_status = ParseShardingFromDevice("/device:A_REPLICATED_CORE:-1", 100);
45 EXPECT_FALSE(parse_status.ok());
46
47 parse_status = ParseShardingFromDevice("/device:A_REPLICATED_CORE:55", 100);
48 TF_EXPECT_OK(parse_status.status());
49 EXPECT_EQ(55, core_from_sharding(parse_status.ValueOrDie()));
50
51 parse_status = ParseShardingFromDevice("/device:A_REPLICATED_CORE:100", 100);
52 EXPECT_FALSE(parse_status.ok());
53
54 parse_status = ParseShardingFromDevice("/cpu:0", 100);
55 TF_EXPECT_OK(parse_status.status());
56 EXPECT_EQ(-1, core_from_sharding(parse_status.ValueOrDie()));
57 }
58
59 class ShardingWithMetadataTest
60 : public ::testing::TestWithParam<xla::OpSharding> {};
61
TEST_P(ShardingWithMetadataTest,GetShardingFromNode)62 TEST_P(ShardingWithMetadataTest, GetShardingFromNode) {
63 NodeDef node_def;
64 {
65 node_def.set_op("_Arg");
66 node_def.set_name("arg");
67 AttrValue xla_sharding;
68 xla_sharding.set_s("");
69 AttrValue index;
70 index.set_i(0);
71 AttrValue type;
72 type.set_type(DataType::DT_FLOAT);
73 node_def.mutable_attr()->insert(
74 {{"_XlaSharding", xla_sharding}, {"index", index}, {"T", type}});
75 }
76
77 auto check_metadata = [](const xla::OpSharding& sharding) {
78 ASSERT_EQ(sharding.metadata_size(), 1);
79 const auto& metadata = sharding.metadata(0);
80 EXPECT_EQ(metadata.op_type(), "_Arg");
81 EXPECT_EQ(metadata.op_name(), "arg");
82 };
83
84 auto test_sharding_metadata =
85 [&check_metadata](
86 const std::function<StatusOr<std::optional<xla::OpSharding>>()>& fn) {
87 auto status_or_sharding = fn();
88 TF_ASSERT_OK(status_or_sharding.status());
89 ASSERT_TRUE(status_or_sharding.ValueOrDie().has_value());
90 auto& sharding = status_or_sharding.ValueOrDie();
91 ASSERT_TRUE(sharding.has_value());
92 if (sharding->type() == xla::OpSharding::TUPLE) {
93 EXPECT_TRUE(sharding->metadata().empty());
94 for (const auto& sharding_element : sharding->tuple_shardings()) {
95 check_metadata(sharding_element);
96 }
97 } else {
98 check_metadata(sharding.value());
99 }
100 };
101
102 {
103 test_sharding_metadata([&node_def]() {
104 return GetShardingFromNodeDef(node_def, /*add_metadata=*/true);
105 });
106 }
107
108 {
109 test_sharding_metadata([&node_def]() {
110 return ParseShardingFromDevice(node_def, /*num_cores_per_replica=*/1,
111 /*add_metadata=*/true);
112 });
113 }
114
115 {
116 Graph graph(OpRegistry::Global());
117 Status status;
118 Node* node = graph.AddNode(node_def, &status);
119 TF_ASSERT_OK(status);
120
121 test_sharding_metadata([node]() {
122 return ParseShardingFromDevice(*node, /*num_cores_per_replica=*/1,
123 /*add_metadata=*/true);
124 });
125 }
126 }
127
CreateTupleSharding()128 xla::OpSharding CreateTupleSharding() {
129 xla::OpSharding sharding;
130 sharding.set_type(xla::OpSharding::TUPLE);
131 sharding.add_tuple_shardings()->set_type(xla::OpSharding::REPLICATED);
132 sharding.add_tuple_shardings()->set_type(xla::OpSharding::REPLICATED);
133 return sharding;
134 }
135
136 INSTANTIATE_TEST_SUITE_P(GetShardingFromNode, ShardingWithMetadataTest,
137 ::testing::Values(xla::sharding_builder::Replicate(),
138 CreateTupleSharding()));
139
140 } // namespace tensorflow
141