ced65fa9b0
Expose a new method `RunHloPassesAndBufferAssignement` on the xla::Compiler in order to be able to get back from XLA into MLIR after the buffer assignment phase. This method needs to be implemented by the platforms that intend to use this MLIR pass. PiperOrigin-RevId: 307969958 Change-Id: I23c9ab04ee5471650367c2b5469fc609c6609114
131 lines
4.4 KiB
C++
131 lines
4.4 KiB
C++
/* 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 "tensorflow/compiler/xla/service/compiler.h"
|
|
|
|
#include <string>
|
|
#include <utility>
|
|
|
|
#include "tensorflow/compiler/xla/types.h"
|
|
#include "tensorflow/compiler/xla/util.h"
|
|
#include "tensorflow/core/platform/logging.h"
|
|
#include "tensorflow/core/platform/macros.h"
|
|
|
|
namespace xla {
|
|
|
|
/* static */ tensorflow::mutex Compiler::platform_compiler_mutex_(
|
|
tensorflow::LINKER_INITIALIZED);
|
|
|
|
StatusOr<
|
|
std::tuple<std::unique_ptr<HloModule>, std::unique_ptr<BufferAssignment>>>
|
|
Compiler::RunHloPassesAndBufferAssignement(
|
|
std::unique_ptr<HloModule> module, se::StreamExecutor* executor,
|
|
se::DeviceMemoryAllocator* device_allocator) {
|
|
return Unimplemented("This compiler does not support this method");
|
|
}
|
|
|
|
std::vector<std::unique_ptr<tensorflow::protobuf::Message>>
|
|
Compiler::ComputeBackendConfigs(const HloInstruction& hlo,
|
|
se::StreamExecutor* executor) const {
|
|
CHECK(executor != nullptr);
|
|
return {};
|
|
}
|
|
|
|
std::unique_ptr<tensorflow::protobuf::Message>
|
|
Compiler::ComputeDefaultBackendConfig(const HloInstruction& hlo,
|
|
se::StreamExecutor* executor) const {
|
|
CHECK(executor != nullptr);
|
|
return nullptr;
|
|
}
|
|
|
|
// Define a default version where metadata is not used.
|
|
StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
|
|
Compiler::CompileAheadOfTime(
|
|
std::unique_ptr<HloModuleGroup> module_group,
|
|
const AotCompilationOptions& options,
|
|
std::unique_ptr<AotCompilationMetadata>* metadata) {
|
|
if (metadata != nullptr) {
|
|
return Unimplemented(
|
|
"Populating AotCompilationMetadata is not implemented on this "
|
|
"compiler.");
|
|
}
|
|
return CompileAheadOfTime(std::move(module_group), options);
|
|
}
|
|
|
|
/* static */ std::map<se::Platform::Id, Compiler::CompilerFactory>*
|
|
Compiler::GetPlatformCompilerFactories() {
|
|
static auto* r = new std::map<se::Platform::Id, CompilerFactory>;
|
|
return r;
|
|
}
|
|
|
|
/* static */
|
|
std::map<se::Platform::Id, std::unique_ptr<Compiler>>*
|
|
Compiler::GetPlatformCompilers() {
|
|
static auto* r = new std::map<se::Platform::Id, std::unique_ptr<Compiler>>;
|
|
return r;
|
|
}
|
|
|
|
/* static */ void Compiler::RegisterCompilerFactory(
|
|
se::Platform::Id platform_id,
|
|
std::function<std::unique_ptr<Compiler>()> compiler_factory) {
|
|
tensorflow::mutex_lock lock(platform_compiler_mutex_);
|
|
auto* factories = GetPlatformCompilerFactories();
|
|
CHECK(factories->find(platform_id) == factories->end())
|
|
<< "Compiler factory already registered for platform";
|
|
(*factories)[platform_id] = std::move(compiler_factory);
|
|
}
|
|
|
|
/* static */ StatusOr<Compiler*> Compiler::GetForPlatform(
|
|
const se::Platform* platform) {
|
|
tensorflow::mutex_lock lock(platform_compiler_mutex_);
|
|
|
|
auto* compilers = GetPlatformCompilers();
|
|
// See if we already instantiated a compiler for this platform.
|
|
{
|
|
auto it = compilers->find(platform->id());
|
|
if (it != compilers->end()) {
|
|
return it->second.get();
|
|
}
|
|
|
|
// If not, we just fall through to try to create one with a registered
|
|
// factory.
|
|
}
|
|
|
|
auto* factories = GetPlatformCompilerFactories();
|
|
auto it = factories->find(platform->id());
|
|
if (it == factories->end()) {
|
|
string hint;
|
|
if (platform->Name() == "Host") {
|
|
hint = " (hint: try linking in tensorflow/compiler/jit:xla_cpu_jit)";
|
|
} else if (platform->Name() == "CUDA") {
|
|
hint = " (hint: try linking in tensorflow/compiler/jit:xla_gpu_jit)";
|
|
}
|
|
|
|
return NotFound(
|
|
"could not find registered compiler for platform %s -- check "
|
|
"target linkage%s",
|
|
platform->Name(), hint);
|
|
}
|
|
|
|
// And then we invoke the factory, placing the result into the mapping.
|
|
compilers->insert(std::make_pair(platform->id(), it->second()));
|
|
return compilers->at(platform->id()).get();
|
|
}
|
|
|
|
AotCompilationOptions::AotCompilationOptions()
|
|
: debug_options_(GetDebugOptionsFromFlags()) {}
|
|
|
|
} // namespace xla
|