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
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
|
/* Copyright 2017 The TensorFlow Authors. 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.
==============================================================================*/
#include <algorithm>
#include <memory>
#include <string>
#include "tensorflow/compiler/xla/array2d.h"
#include "tensorflow/compiler/xla/client/local_client.h"
#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
#include "tensorflow/compiler/xla/client/xla_computation.h"
#include "tensorflow/compiler/xla/literal.h"
#include "tensorflow/compiler/xla/ptr_util.h"
#include "tensorflow/compiler/xla/reference_util.h"
#include "tensorflow/compiler/xla/shape_util.h"
#include "tensorflow/compiler/xla/statusor.h"
#include "tensorflow/compiler/xla/test_helpers.h"
#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
#include "tensorflow/compiler/xla/tests/literal_test_util.h"
#include "tensorflow/compiler/xla/tests/test_macros.h"
#include "tensorflow/compiler/xla/tests/test_utils.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/types.h"
namespace xla {
namespace {
#ifdef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
using TypesF16F32 = ::testing::Types<float>;
#else
using TypesF16F32 = ::testing::Types<Eigen::half, float>;
#endif
class MatOpsSimpleTest : public ClientLibraryTestBase {};
template <typename T>
class MatOpsSimpleTest_F16F32 : public MatOpsSimpleTest {};
TYPED_TEST_CASE(MatOpsSimpleTest_F16F32, TypesF16F32);
XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, ExpTwoByTwoValues) {
using T = TypeParam;
XlaBuilder builder("exp_2x2");
auto data = ConstantR2FromArray2D<T>(&builder, {
{1.0f, 0.0f}, // row 0
{-1.0f, 0.5f}, // row 1
});
Exp(data);
std::unique_ptr<Literal> expected =
LiteralUtil::CreateR2FromArray2D<T>({{2.71828f, 1.00000f}, // row 0
{0.36788f, 1.64872f}}); // row 1
this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5));
}
XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MapTwoByTwo) {
using T = TypeParam;
XlaComputation add_half;
{
// add_half(x) = x + 0.5
XlaBuilder builder("add_half");
auto x_value =
Parameter(&builder, 0, ShapeUtil::MakeShapeWithType<T>({}), "x_value");
auto half = ConstantR0<T>(&builder, static_cast<T>(0.5));
Add(x_value, half);
auto computation_status = builder.Build();
ASSERT_IS_OK(computation_status.status());
add_half = computation_status.ConsumeValueOrDie();
}
XlaBuilder builder("map_2x2");
auto data = ConstantR2FromArray2D<T>(&builder, {
{1.0f, 0.0f}, // row 0
{-1.0f, 0.5f}, // row 1
});
Map(&builder, {data}, add_half, {0, 1});
std::unique_ptr<Literal> expected =
LiteralUtil::CreateR2FromArray2D<T>({{1.5f, 0.5f}, // row 0
{-0.5f, 1.0f}}); // row 1
this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-5));
}
XLA_TYPED_TEST(MatOpsSimpleTest_F16F32, MaxTwoByTwoValues) {
using T = TypeParam;
XlaBuilder builder("max_2x2");
auto lhs = ConstantR2FromArray2D<T>(&builder, {
{7.0f, 2.0f}, // row 0
{3.0f, -4.0f}, // row 1
});
auto rhs = ConstantR2FromArray2D<T>(&builder, {
{5.0f, 6.0f}, // row 0
{1.0f, -8.0f}, // row 1
});
Max(lhs, rhs);
std::unique_ptr<Literal> expected =
LiteralUtil::CreateR2FromArray2D<T>({{7.0f, 6.0f}, // row 0
{3.0f, -4.0f}}); // row 1
this->ComputeAndCompareLiteral(&builder, *expected, {}, ErrorSpec(1e-6));
}
struct TestLinspaceMaxParam {
int64 rows;
int64 cols;
};
class TestLinspaceMaxParametric
: public MatOpsSimpleTest,
public ::testing::WithParamInterface<TestLinspaceMaxParam> {
public:
template <typename T>
void TestImpl() {
TestLinspaceMaxParam param = GetParam();
int64 rows = param.rows;
int64 cols = param.cols;
float from = -128.0, to = 256.0;
std::unique_ptr<Array2D<T>> alhs =
MakeLinspaceArray2D<T>(from, to, rows, cols);
auto arhs = MakeUnique<Array2D<T>>(rows, cols, static_cast<T>(1.0f));
XlaBuilder builder(
tensorflow::strings::Printf("max_%lldx%lld_linspace", rows, cols));
auto lhs = ConstantR2FromArray2D<T>(&builder, *alhs);
auto rhs = ConstantR2FromArray2D<T>(&builder, *arhs);
Max(lhs, rhs);
Array2D<T> expected(rows, cols);
for (int row = 0; row < rows; ++row) {
for (int col = 0; col < cols; ++col) {
expected(row, col) = std::max<T>((*alhs)(row, col), (*arhs)(row, col));
}
}
ErrorSpec error_spec(1e-6);
if (std::is_same<Eigen::half, T>::value) {
error_spec = ErrorSpec(1e-6, 2e-4);
}
ComputeAndCompareR2<T>(&builder, expected, {}, error_spec);
}
};
string PrintTestLinspaceMaxParam(
const ::testing::TestParamInfo<TestLinspaceMaxParam>& test_param) {
const TestLinspaceMaxParam& param = test_param.param;
return tensorflow::strings::StrCat(param.rows, "r", param.cols, "c");
}
#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
XLA_TEST_P(TestLinspaceMaxParametric, TestF16) { TestImpl<Eigen::half>(); }
#endif
XLA_TEST_P(TestLinspaceMaxParametric, TestF32) { TestImpl<float>(); }
INSTANTIATE_TEST_CASE_P(
TestLinspaceMax, TestLinspaceMaxParametric,
::testing::Values(TestLinspaceMaxParam{1, 1}, TestLinspaceMaxParam{2, 2},
TestLinspaceMaxParam{3, 3}, TestLinspaceMaxParam{4, 4},
TestLinspaceMaxParam{6, 6}, TestLinspaceMaxParam{8, 8},
TestLinspaceMaxParam{12, 12},
TestLinspaceMaxParam{16, 16}, TestLinspaceMaxParam{32, 8},
TestLinspaceMaxParam{64, 8}),
PrintTestLinspaceMaxParam);
class MatOpsDotAddTest
: public ClientLibraryTestBase,
public ::testing::WithParamInterface<std::tuple<bool, bool, bool>> {
public:
template <typename T>
void TestImpl() {
bool row_major = std::get<0>(GetParam());
bool add_lhs = std::get<1>(GetParam());
bool transpose = std::get<2>(GetParam());
Array2D<T> lhs({{1.0f, 2.0f}, {3.0f, 4.0f}});
Array2D<T> rhs({{10.0f, 11.0f}, {12.0f, 13.0f}});
auto minor_to_major = [](bool row_major) -> std::vector<int64> {
return {row_major ? 1 : 0, row_major ? 0 : 1};
};
auto prim_type = primitive_util::NativeToPrimitiveType<T>();
Shape lhs_shape =
ShapeUtil::MakeShape(prim_type, {lhs.height(), lhs.width()});
Shape rhs_shape =
ShapeUtil::MakeShape(prim_type, {rhs.height(), rhs.width()});
TF_ASSERT_OK_AND_ASSIGN(
auto lhs_handle,
client_->TransferToServer(
*LiteralUtil::CreateR2FromArray2DWithLayout<T>(
lhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
TF_ASSERT_OK_AND_ASSIGN(
auto rhs_handle,
client_->TransferToServer(
*LiteralUtil::CreateR2FromArray2DWithLayout<T>(
rhs, LayoutUtil::MakeLayout(minor_to_major(row_major)))));
XlaBuilder builder(TestName());
auto lhs_arg = Parameter(&builder, 0, lhs_shape, "lhs");
auto lhs_mat_arg = lhs_arg;
if (transpose) {
lhs_mat_arg = Transpose(lhs_mat_arg, {1, 0});
}
auto rhs_arg = Parameter(&builder, 1, rhs_shape, "rhs");
auto result = Dot(lhs_mat_arg, rhs_arg);
Array2D<T> expected;
if (add_lhs) {
result = Add(result, lhs_arg);
if (transpose) {
expected = Array2D<T>({{47.0f, 52.0f}, {71.0f, 78.0f}});
} else {
expected = Array2D<T>({{35.0f, 39.0f}, {81.0f, 89.0f}});
}
} else {
result = Add(result, rhs_arg);
if (transpose) {
expected = Array2D<T>({{56.0f, 61.0f}, {80.0f, 87.0f}});
} else {
expected = Array2D<T>({{44.0f, 48.0f}, {90.0f, 98.0f}});
}
}
ComputeAndCompareR2<T>(&builder, expected,
{lhs_handle.get(), rhs_handle.get()},
ErrorSpec(1e-6));
}
};
XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2BF16) { TestImpl<bfloat16>(); }
#ifndef XLA_BACKEND_DOES_NOT_SUPPORT_FLOAT16
XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2F16) { TestImpl<Eigen::half>(); }
#endif
XLA_TEST_P(MatOpsDotAddTest, Dot_Add_2x2_2x2F32) { TestImpl<float>(); }
INSTANTIATE_TEST_CASE_P(MatOpsDotAddTestInstances, MatOpsDotAddTest,
::testing::Combine(::testing::Bool(), ::testing::Bool(),
::testing::Bool()));
} // namespace
} // namespace xla
|
