109 lines
4.0 KiB
C++
109 lines
4.0 KiB
C++
/* Copyright 2018 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 "tensorflow/compiler/jit/xla_compilation_cache.h"
|
|
|
|
#include "tensorflow/compiler/jit/flags.h"
|
|
#include "tensorflow/compiler/tf2xla/shape_util.h"
|
|
#include "tensorflow/compiler/xla/client/client_library.h"
|
|
#include "tensorflow/core/platform/test.h"
|
|
#include "tensorflow/core/platform/test_benchmark.h"
|
|
|
|
namespace tensorflow {
|
|
namespace {
|
|
|
|
TEST(XlaCompilationCacheTest, SignatureEquality) {
|
|
NameAttrList fn;
|
|
fn.set_name("afunction");
|
|
std::vector<XlaCompiler::Argument> args(1);
|
|
args[0].kind = XlaCompiler::Argument::kConstant;
|
|
args[0].type = DT_INT32;
|
|
args[0].shape = TensorShape({4, 0});
|
|
args[0].constant_value = Tensor(DT_INT32, {4, 0});
|
|
TF_ASSERT_OK_AND_ASSIGN(XlaCompilationCache::Signature s1,
|
|
XlaCompilationCache::BuildSignature(fn, args));
|
|
|
|
args[0].type = DT_FLOAT;
|
|
args[0].constant_value = Tensor(DT_FLOAT, {4, 0});
|
|
TF_ASSERT_OK_AND_ASSIGN(XlaCompilationCache::Signature s2,
|
|
XlaCompilationCache::BuildSignature(fn, args));
|
|
|
|
args[0].shape = TensorShape({0, 4});
|
|
args[0].constant_value = Tensor(DT_FLOAT, {0, 4});
|
|
TF_ASSERT_OK_AND_ASSIGN(XlaCompilationCache::Signature s3,
|
|
XlaCompilationCache::BuildSignature(fn, args));
|
|
|
|
std::vector<XlaCompilationCache::Signature> signatures = {s1, s2, s3};
|
|
for (int i = 0; i < signatures.size(); ++i) {
|
|
for (int j = 0; j < signatures.size(); ++j) {
|
|
EXPECT_EQ(i == j, signatures[i] == signatures[j])
|
|
<< signatures[i].HumanString() << " " << signatures[j].HumanString();
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST(XlaCompilationCacheTest, TestDisabledXlaCompilation) {
|
|
NameAttrList fn;
|
|
fn.set_name("afunction");
|
|
|
|
DisableXlaCompilation();
|
|
|
|
xla::LocalClient* client = xla::ClientLibrary::LocalClientOrDie();
|
|
DeviceType device_type = DeviceType(DEVICE_CPU_XLA_JIT);
|
|
|
|
const XlaCompiler::CompilationResult* compilation_result;
|
|
xla::LocalExecutable* executable;
|
|
|
|
auto cache = new XlaCompilationCache(client, device_type);
|
|
core::ScopedUnref cache_ref(cache);
|
|
|
|
Status status = cache->Compile(XlaCompiler::Options{}, fn, {},
|
|
XlaCompiler::CompileOptions{},
|
|
XlaCompilationCache::CompileMode::kStrict,
|
|
&compilation_result, &executable);
|
|
EXPECT_FALSE(status.ok());
|
|
EXPECT_TRUE(
|
|
absl::StrContains(status.error_message(), "XLA compilation disabled"));
|
|
}
|
|
|
|
void BM_BuildSignature(::testing::benchmark::State& state) {
|
|
const int n_args = state.range(0);
|
|
|
|
NameAttrList fn;
|
|
fn.set_name("afunction");
|
|
for (int i = 0; i < n_args; i++) {
|
|
(*fn.mutable_attr())[absl::StrCat("T", i)].set_type(DT_FLOAT);
|
|
}
|
|
std::vector<XlaCompiler::Argument> args(n_args);
|
|
for (int i = 0; i < n_args; i++) {
|
|
args[i].kind = (((i % 3) == 0) ? XlaCompiler::Argument::kConstant
|
|
: XlaCompiler::Argument::kParameter);
|
|
args[i].type = DT_INT32;
|
|
args[i].shape = TensorShape({4, 0});
|
|
args[i].constant_value = Tensor(DT_INT32, {4, 0});
|
|
}
|
|
|
|
for (auto i : state) {
|
|
xla::StatusOr<XlaCompilationCache::Signature> s =
|
|
XlaCompilationCache::BuildSignature(fn, args);
|
|
CHECK(s.ok());
|
|
XlaCompilationCache::Signature sig = std::move(s.ValueOrDie());
|
|
}
|
|
}
|
|
BENCHMARK(BM_BuildSignature)->Arg(0)->Arg(1)->Arg(2)->Arg(5)->Arg(10);
|
|
|
|
} // namespace
|
|
} // namespace tensorflow
|