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/* 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/gpu/sequential_thunk.h"
#include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h"
#include "tensorflow/core/lib/core/errors.h"
namespace xla {
namespace gpu {
SequentialThunk::SequentialThunk(std::vector<std::unique_ptr<Thunk>> thunks,
const HloInstruction* hlo)
: Thunk(Kind::kSequential, hlo), thunks_(std::move(thunks)) {}
Status SequentialThunk::Initialize(const GpuExecutable& executable,
se::StreamExecutor* executor) {
for (auto& thunk : thunks_) {
TF_RETURN_IF_ERROR(thunk->Initialize(executable, executor));
}
return Status::OK();
}
Status SequentialThunk::ExecuteOnStream(
const BufferAllocations& buffer_allocations, se::Stream* stream,
HloExecutionProfiler* profiler) {
// TODO(b/71544591): We need to potentially measure the total time of the
// sequential thunk. This happens for a reduce op which consists of
// SequentialThunk with a thunk that initializes the output, and another thunk
// that does the actual reduce. Right now, in this case we would only measure
// the time of the last thunk, because both thunks would have the same
// HloInstruction.
for (const auto& thunk : thunks_) {
TF_RETURN_IF_ERROR(
thunk->ExecuteOnStream(buffer_allocations, stream, profiler));
}
return Status::OK();
}
} // namespace gpu
} // namespace xla
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